Geology topics

Justin W. Van Zee

Publications and source records attributed to Justin W. Van Zee.

8 recordsLinked to original sources

USGS2025

Optimizing sampling across transect-based methods improves the power of agroecological monitoring data

Transect-based monitoring has long been a valuable tool in ecosystem monitoring to measure multiple ecosystem attributes. The line-point intercept (LPI), vegetation height, and canopy gap intercept methods comprise a set of core methods, which provide indicators of ecosystem condition. However, users often struggle to design a sampling strategy that optimizes the ability to detect ecological change using transect-based methods. We assessed the sensitivity of each of these core methods to transect length, number, and sampling interval in 1-ha plots to determine: (1) minimum sampling required to describe ecosystem characteristics and detect change; and (2) optimal transect length and number to make recommendations for future analyses and monitoring efforts. We used data from 13 National Wind Erosion Research Network locations, including five LTAR sites, spanning the western United States, which included 151 plot sampling events over time across five biomes. We found that longer and increased replicates of transects were more important for reducing sampling error than increased sample intensity along fewer transects per plot. For all methods and indicators across biomes plots, three 100-m transects reduced sampling error such that indicator estimates fell within a 95% confidence interval of ±5% for canopy gap intercept and LPI-total foliar cover, ±5 cm for height, and ±2 species for LPI-species counts. For the same criteria at 80% confidence intervals, two 100-m transects are needed. Site-scale inference was strongly affected by sample design, consequently our understanding of ecological dynamics may be influenced by sampling decisions.

western United States↗

USGS2023

The Landscape Data Commons: A system for standardizing, accessing, and applying large environmental datasets for agroecosystem research and management

Understanding where, when, and why agroecosystems are changing requires quality information about ecosystems that span land tenure, ecological processes, and spatial scales. Over the past two decades, land management agencies and research groups have adopted a suite of standardized methods for monitoring rangelands, which have been implemented at over 85,000 monitoring locations globally. However, the ability to use these data to understand agroecosystem dynamics and change across scales and across land ownership has been limited because, until now, these data have not been available in a harmonized, accessible format for analyses, modeling, and decision-support tools. We present the Landscape Data Commons, a cyberinfrastructure platform that harmonizes and aggregates standardized agroecosystem data, enables linkages to models, and facilitates analysis and interpretation of data within decision-support tools. The Landscape Data Commons provides a community platform for users to contribute data and develop next-generation tools to support agroecosystem management through the 21st century.

Agricultural & Environmental Letters↗

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

Ten practical questions to improve data quality

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.

Rangelands↗

USGS2019

Reducing sampling uncertainty in aeolian research to improve change detection

Measurements of aeolian sediment transport support our understanding of mineral dust impacts on Earth and human systems and assessments of aeolian process sensitivities to global environmental change. However, sample design principles are often overlooked in aeolian research. Here, we use high‐density field measurements of sediment mass flux across land use and land cover types to examine sample size and power effects on detecting change in aeolian transport. Temporal variances were 1.6 to 10.1 times the magnitude of spatial variances in aeolian transport for six study sites. Differences in transport were detectable for >67% of comparisons among sites using ~27 samples. Failure to detect change with smaller sample sizes suggests that aeolian transport measurements and monitoring are much more uncertain than recognized. We show how small and selective sampling, common in aeolian research, gives the false impression that differences in aeolian transport can be detected, potentially undermining inferences about process and impacting reproducibility of aeolian research.

Journal of Geophysical Research F: Earth Surface↗

USGS2017

Biological soil crust and disturbance controls on surface hydrology in a semi-arid ecosystem

Biological soil crust communities (biocrusts) play an important role in surface hydrologic processes in dryland ecosystems, and these processes may then be dramatically altered with soil surface disturbance. In this study, we examined biocrust hydrologic responses to disturbance at different developmental stages on sandy soils on the Colorado Plateau. Our results showed that all disturbance (trampling, scalping and trampling+scalping) of the early successional light cyanobacterial biocrusts generally reduced runoff. In contrast, trampling well-developed dark-cyano-lichen biocrusts increased runoff and sediment loss relative to intact controls. Scalping did not increase runoff, implying that soil aggregate structure was important to the infiltration process. Well-developed, intact dark biocrusts generally had lower runoff, low sediment loss, and highest aggregate stability whereas the less-developed light biocrusts were highest in runoff and sediment loss when compared to the controls. These results suggest the importance of maintaining the well-developed dark biocrusts, as they are beneficial for lowering runoff and reducing soil loss and redistribution on the landscape. These data also suggest that upslope patches of light biocrust may either support water transport to downslope vegetation patches or alternatively this runoff may place dark biocrust patches at risk of disruption and loss, given that light patches increase runoff and thus soil erosion potential.

Colorado↗

USGS2017

Enhancing wind erosion monitoring and assessment for U.S. rangelands

On the Ground Wind erosion is a major resource concern for rangeland managers because it can impact soil health, ecosystem structure and function, hydrologic processes, agricultural production, and air quality. Despite its significance, little is known about which landscapes are eroding, by how much, and when. The National Wind Erosion Research Network was established in 2014 to develop tools for monitoring and assessing wind erosion and dust emissions across the United States. The Network, currently consisting of 13 sites, creates opportunities to enhance existing rangeland soil, vegetation, and air quality monitoring programs. Decision-support tools developed by the Network will improve the prediction and management of wind erosion across rangeland ecosystems.

Rangelands↗

USGS2016

The National Wind Erosion Research Network: Building a standardized long-term data resource for aeolian research, modeling and land management

The National Wind Erosion Research Network was established in 2014 as a collaborative effort led by the United States Department of Agriculture’s Agricultural Research Service and Natural Resources Conservation Service, and the United States Department of the Interior’s Bureau of Land Management, to address the need for a long-term research program to meet critical challenges in wind erosion research and management in the United States. The Network has three aims: (1) provide data to support understanding of basic aeolian processes across land use types, land cover types, and management practices, (2) support development and application of models to assess wind erosion and dust emission and their impacts on human and environmental systems, and (3) encourage collaboration among the aeolian research community and resource managers for the transfer of wind erosion technologies. The Network currently consists of thirteen intensively instrumented sites providing measurements of aeolian sediment transport rates, meteorological conditions, and soil and vegetation properties that influence wind erosion. Network sites are located across rangelands, croplands, and deserts of the western US. In support of Network activities, http://winderosionnetwork.org was developed as a portal for information about the Network, providing site descriptions, measurement protocols, and data visualization tools to facilitate collaboration with scientists and managers interested in the Network and accessing Network products. The Network provides a mechanism for engaging national and international partners in a wind erosion research program that addresses the need for improved understanding and prediction of aeolian processes across complex and diverse land use types and management practices.

Arizona, California, Colorado, Idaho, North Dakota↗