@booklet {JRN55376, title = {sample.design}, year = {2022}, month = {Zenodo}, abstract = {One of the major components of ecological research and land management is data collection. Creating sampling designs which are statistically valid and intercompatible can be difficult. The R language software package sample design contains a collection of functions which can be used to facilitate the creation of statistically valid and spatially balanced sampling designs. These include functions for checking the spatial balance of sampling designs, determining how to allocate sampling effort, combine multiple sampling designs, and more. The package sample.design currently in use by the Bureau of Land Management as part of their Assessment, Inventory, and Monitoring program.}, doi = {10.5281/zenodo.6518745}, author = {Nelson Stauffer and S. L. Garman} } @conference {JRN55356, title = {A web interface for exploring and setting land management benchmarks}, booktitle = {Society for Range Management Annual Meeting 2022}, year = {2022}, month = {2022}, abstract = {Land managers make their decisions informed by ~the best available data. Data require interpretation in order to be applied to management decisions, however. One method for interpreting quantitative data is the application of quantitative benchmarks. Benchmarks are values or ranges of values for a given measured indicator of landscape function which can be used to classify the data into management categories such as {\textquotedblleft}meeting{\textquotedblright} and {\textquotedblleft}not meeting{\textquotedblright} objectives or {\textquotedblleft}unsuitable,{\textquotedblright} {\textquotedblleft}marginal,{\textquotedblright} and {\textquotedblleft}suitable{\textquotedblright} habitat. Importantly, benchmarks can be tied to covariates like soils, landforms, or ecological sites, which then enables the application of benchmarks as a layer of interpretation. This practice is already widespread with applications like the Sage-Grouse Habitat Assessment Framework which uses benchmarks tied to season of use to classify measured indicator values according to habitat suitability classes. However, most potential use cases for benchmarks start with the difficult determination of the benchmark values for each classification category. In order to empower land managers to set their benchmarks, we have created a web application, the Benchmark Exploration Tool. This tool allows users to upload their own monitoring data or download data from the Landscape Data Commons and visualize them as histograms. Users are able to set quantiles to plot in the generated figures in order to better intuit the distribution of their data. Users can also set and apply benchmarks, based on policy, local knowledge of ecosystem structure and function, or other sources. This allows users to see how different benchmark values might affect their interpretation of the data. All figures and tables produced by the tool can be downloaded for use in reports or other documentation. With this tool, land managers can better understand their data and establish data-driven benchmarks for their management efforts.}, author = {Nelson Stauffer and Alexander Laurance-Traynor and Sarah McCord} } @article {JRN55330, title = {Sampling design workflows and tools to support adaptive monitoring and management}, journal = {Rangelands}, year = {2021}, month = {9/30/2021}, pages = {1-9}, abstract = {On the Ground {\textbullet}Adaptive land management requires monitoring of resource conditions, which requires choices about where and when to monitor a landscape. {\textbullet}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. {\textbullet}Here, we provide descriptions of each step in the process and identify tools and resources to complete each step.}, keywords = {data collection, monitoring, monitoring workflow, sample design, spatially balanced sampling, stratification}, doi = {10.1016/j.rala.2021.08.005}, url = {files/bibliography/21-040.pdf}, author = {Nelson Stauffer and Danielle Duni and Jason W. Karl and Travis Nauman} } @conference {JRN55241, title = {A web interface for creating random, spatially balanced landscape monitoring designs}, booktitle = {Society for Range Management}, year = {2019}, month = {02/2020}, address = {Denver, Colorado}, abstract = {In the monitoring and assessment of landscapes, randomly located sampling plots are often used to minimize sampling bias and enable inference to larger landscape units. In particular, spatially balanced random designs are more robust to spatial autocorrelation and therefore produce data usable for answering multiple management questions. The Generalized Random Tessellation Stratified (GRTS) approach is one technique for creating spatially balanced random designs in natural resources and has been widely adopted by multi-scale terrestrial and aquatic monitoring programs. While the parameters for a GRTS design are relatively simple to specify with stratification polygons and per-stratum sample sizes, generating a design from those inputs has historically required the ability to code using the R package spsurvey. As a result, technical skills have been a bottleneck for this statistical approach in resource monitoring programs. To remove this technological barrier, we have developed a web application, the {\textquotedblleft}Balanced Design Tool{\textquotedblright}, to create GRTS designs through a graphical interface. The tool prompts users to upload polygons describing their study area and stratification then to specify the number of points to draw per stratum before generating a spatially balanced design. Version 1.5, released in October 2019, added significant functionality. The point allocation process now supports three different approaches (manual, proportional by stratum area, and equally by stratum) through a more user-friendly interface. Users can now use an interactive, webmap to evaluate their designs without needing mapping software (e.g. ArcGIS). The final downloaded design now includes all the files (including an R script) to recreate the design and design documentation. With this tool, land managers can now create their own reproducible, spatially balanced designs to develop locally appropriate, statistically valid monitoring programs to suit a wide variety of objectives. Poster Pod 43, $\#$92.}, author = {Nelson Stauffer and Sarah McCord and Jason W. Karl} } @conference {JRN54911, title = {Communicating Ecological Data with Land Managers: Lessons Learned}, booktitle = {Ecological Society of America}, year = {2018}, month = {08/2018}, publisher = {Ecological Society of America}, organization = {Ecological Society of America}, address = {New Orleans, LA}, abstract = {

Communicating ecological data to land managers and management entities represents a different challenge than communicating with the general public. Land managers need to not only understand results but also the inherent limits and assumptions in order to make effective and legally defensible decisions. However, many face barriers to producing or interpreting results of data analysis including time, resources, and technical skills. Our group, a partnership between the USDA Agricultural Research Service (ARS), the National Aquatic Monitoring Center (NAMC), and the Bureau of Land Management (BLM), has been working on the BLM Assessment, Inventory, and Monitoring Strategy (AIM) since 2010. The purpose of AIM is to provide land managers with robust, high quality monitoring data gathered with consistent methods and statistically-sound sampling techniques. Through this effort, we have experienced both successes and failures in communicating data to land managers.
As a result, our data communication has necessarily evolved. We refined the amount of data, their format, and the kinds of results and delivery mechanisms. This process has required working iteratively with land managers to identify what analyses and data visualizations are useful and how to make them as accessible as possible. Results/Conclusions: Based on feedback from users and observing how data have been applied, we have arrived at general principles for communicating data to land managers. We find that starting with conversation about users{\textquoteright} analysis objectives and existing decision-making workflows that use the results often shapes data requests. High density, high complexity figures are less likely to be impactful; we try to consistently produce simple, direct figures which display only one kind of result. Land managers often find graphical figures easier to draw general conclusions from, but may need tabular results for documentation; we provide equivalent data in multiple formats which can be integrated into different analyses. We aim to present statistics in ways that users can interrogate and discuss as part of interpretation; this is especially important for statistics like uncertainty that users may have intuitive rather than formal understandings of.Our conclusions are shaped by direct feedback from land managers regarding what helps and hinders their use of AIM data. We will continue refining our approaches through workshops with the community of practice of land managers who are using the data.

}, author = {Nelson Stauffer and Sarah McCord and Emily Kachergis} } @proceedings {JRN54858, title = {DIMA.Tools: An R package for working with the database for inventory, monitoring, and assessment}, volume = {January 28-February 2, 2018}, year = {2018}, month = {01/28/2018}, publisher = {Society for Range Management Meeting Proceedings}, address = {Reno, NV}, abstract = {

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.

}, author = {Nelson Stauffer and Sarah McCord} } @conference {JRN54673, title = {Building software tools to help contextualize and interpret monitoring data}, booktitle = {70th Annual Society for Range Managment}, volume = {Jan 29 - Feb 02, 2017}, year = {2017}, month = {01/2017}, publisher = {Society for Range Management}, organization = {Society for Range Management}, address = {Saint George, Utah}, abstract = {
Even modest monitoring efforts at landscape scales produce large volumes of data.These are most useful if they can be interpreted relative to land potential or other similar sites. However, for many ecological systems reference conditions may not be defined or are poorly described, which hinders understanding what values for a monitoring indicator are acceptable. One solution for this problem is to mine existing monitoring data to examine the distribution of the indicator values across sites and compare the distributions of subsets of the data against each other. In the case of the BLM{\textquoteright}s Terrestrial Assessment, Inventory, and Monitoring Database (TerrADat), pre-computed indicator values are available for thousands of monitoring locations. \ By grouping these data by geographic region, land potential (e.g. ecological site), or other factors, indicator values in TerrADat can be visualized and analyzed to determine potential indicator ranges, supporting management decisions even though none of the monitoring locations are designated as reference. \ We have developed a web-based statistical tool which streamlines histogram-based exploration of TerrADat, giving users a graphical interface to select survey locations to include and which indicators to use. Users can select data from TerrADat to plot using either an uploaded polygon shapefile to restrict spatially or by metadata queries. Figures can include multiple subsets of TerrADat to compare distributions of indicators between landscape units, e.g. ecological sites or grazing allotments. Additionally, the tool dynamically adapts its output to reflect common sense visualization rules for clarity. Visualizing indicator value ranges from similar areas can allow users to see, if not how conditions compare to reference, at least how conditions compare to the greater context on the landscape. This can be used to help support decisions about prioritization of management effort or as evidence toward defining what reference conditions are for portions of the landscape.
}, author = {Nelson Stauffer and Jason W. Karl} } @conference {JRN54641, title = {Deriving empirical benchmarks from existing monitoring datasets for rangeland adaptive management}, booktitle = {Society for Range Management Meeting}, year = {2016}, month = {01/2016}, publisher = {Society for Range Management Meeting Abstracts}, organization = {Society for Range Management Meeting Abstracts}, address = {Corpus Christi ,TX}, abstract = {
Under adaptive management, goals and decisions for managing rangeland resources are shaped by requirements like the Bureau of Land Management{\textquoteright}s (BLM{\textquoteright}s) Land Health Standards, which specify desired conditions. Without formalized, quantitative benchmarks for triggering management actions, adaptive management is challenging and subjective. Traditionally, monitoring and management benchmarks have been derived from key sites or expert opinion. However, these techniques are limited by time, scale, subjectivity, and may inform only on conditions that are no longer achievable. Empirical approaches for developing benchmarks for different indicators based on a growing corpus of rangeland monitoring data may provide a useful and defensible alternative. We describe use of BLM Assessment, Inventory, and Monitoring program data from northern California to establish quantitative monitoring benchmarks for BLM{\textquoteright}s Land Health Standards. Bare ground, canopy gap, and perennial grass cover were explored as testbed indicators. Benchmarks are necessarily tied to land potential units (e.g., ecological sites) and can be used to assign categories (e.g. {\textquotedblleft}meets objectives{\textquotedblright} and {\textquotedblleft}does not meet objectives{\textquotedblright}). When an indicator{\textquoteright}s value range is broadly distributed and encompasses potential reference conditions, as with bare ground and canopy gap, simple quantiles may be sufficient for setting criteria. For indicators with narrow and skewed distributions due to disturbance or management legacies, (e.g. perennial grass cover), this technique has limited application. Comparing conditions across land potential units is simpler with categories than with numerical values. Additionally, summarizing areas of interest by the proportion of sample units meeting success criteria makes clear that, although an area may overall meet management objectives, portions may not. This approach has potential for broad application, particularly with existing and ever-growing quantitative monitoring datasets available, and complements historic approaches which remain viable options in some cases.
}, author = {Nelson Stauffer and Jason W. Karl and Sarah McCord and Scott W. Miller} }