Monitoring supports iterative learning about the effectiveness of management actions, information that can help managers plan future actions, facilitate decision-making, and improve outcomes.

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.

Adaptive monitoring is connected to research and management through the exchange of data; analytical, methodological, and technological developments; information; and understanding.

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.

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.

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.

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.

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.

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.

Monitoring courses, offered at universities and through professional training, are critical to successfully collecting and applying rangeland monitoring data.

Instructors can meet course objectives by carefully considering course content, the target audience, delivery approaches, evaluation mechanisms, and training for new instructors.

Shared principles and practices taught in monitoring courses facilitate the rangeland management community in achieving desired outcomes through adaptive management.

Interpreting Indicators of Rangeland Health and other well-designed qualitative assessments are useful for understanding ecological function and can be used to prioritize areas for monitoring, restoration, or management changes. When completed by experienced, trained multidisciplinary teams, qualitative assessments provide reliable information about ecological processes and are repeatable across time and geographic locations.

Consistency and repeatability of qualitative assessments are maximized when the assessments are supported by appropriate quantitative indicator data. Likewise, quantitative datasets may be complemented by qualitative assessments, which can provide insight into indicators that are difficult to measure, providing a more complete view of vegetation, soils, and underlying ecological processes.

Qualitative assessments can be used as a communication tool for developing a common understanding of resource issues and a shared vision for, and commitment to future stewardship.

New opportunities are emerging to enable further integration of qualitative and quantitative field protocols, ecological models, and remotely sensed products to benefit rangeland assessment, monitoring, and management.

The BLM Assessment, Inventory, and Monitoring (AIM) strategy recommends five principles for building multiscale monitoring programs: standardized methods and indicators; data management and stewardship; appropriate sample designs; remote sensing integration; and structured implementation. These principles guide monitoring across public lands.

We find the AIM principles are sound and worthy of consideration for design and adaptation of rangeland monitoring programs worldwide.

An emergent principle, standard workflows and analysis frameworks for using data, connects data to land management decision-making and empowers land managers.

The AIM principles inspire and provide opportunities for the rangeland management community to implement adaptive management.

Collection, interpretation, and application of use-based monitoring data across large landscapes is challenging given the inherent variability in growing conditions and field-based estimates.

We present several approaches on leveraging geospatial data and technology to cope with this variability including weather and climate data, satellite remote-sensing data and associated tools, as well as livestock GPS collars.

Field-based estimates also can be improved with more careful consideration of field methods and improved observer training and calibration.

Planning and co-implementing of use-based and long-term landscape monitoring can inform causes of declining or improving rangeland health and better inform adaptive management.

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.

Available rangeland data, from field-measured plots to remotely sensed landscapes, provide much needed information for mapping and modeling wildlife habitats.

Better integration of wildlife habitat characteristics into rangeland monitoring schemes is needed for most rangeland wildlife species at varying spatial and temporal scales.

Here, we aim to stimulate use of and inspire ideas about rangeland monitoring data in the context of wildlife habitat modeling and species conservation.

Use of adaptive management supported by robust monitoring is vital to solving severe rangeland problems, such as the exotic annual grass invasion and fire cycle in sagebrush-steppe rangelands.

Uncertainty in post-fire plant-community composition and plant response to treatments poses a challenge to land management and research but can be addressed with a high density of observations over short time frames.

The monitoring for adaptive management of the 2015 Soda Megafire area (113,000 Ha) sampled up to 2000 observation plots in each of five post-fire years, and provided important insights on challenges, solutions, and insights that can be applied to monitoring future burned areas.

Adaptive management should explicitly involve stakeholders, emphasize multiple iterations of identifying and prioritizing outcomes, and tightly link science-informed monitoring to decision-making benchmarks for effective feedback loops.

Short-term monitoring procedures should be simple, quick, and based on consistent methods that are focused on locations where meaningful change is expected or uncertainty is high.

Long-term monitoring procedures should emphasize consistent methodology across years that provides broader ecosystem context for multiple ecosystem services (e.g., watershed protection and grassland bird habitat).

Incorporating timely feedback from monitoring improves the capacity for rapid decision-making when benchmarks are attained and management should be modified.