Geology topics

Tamzen K. Stringham

Publications and source records attributed to Tamzen K. Stringham.

3 recordsLinked to original sources

USGS2026

Quantifying free-roaming horse body condition: A methodological investigation using game cameras and observer bias corrections

Large populations of free-roaming horses (Equus caballus ) inhabit the western United States, Australia, and other arid and semi-arid rangelands globally. In the United States, horse body condition has been the impetus for gathering and removing horses under an emergency designation and has been noted as a common chronic mortality-causing condition. The Henneke body condition scoring (BCS) system is a qualitative index that includes visual appraisal and physical palpation at multiple anatomical locations on individual horses. This method has been shown to be repeatable between different observers with varying degrees of experience when assessing domestic horses at close proximity. Repeatability using the Henneke BCS system is unknown for free-roaming equids in vast environments, and adequately sampling populations in a minimally biased manner can be logistically challenging. However, estimating body condition using photographs has been shown to be a highly accurate alternative to proximal scoring for multiple scoring systems and species. Our study investigates if an automated, remote camera trap system can be used to estimate free-roaming equid body condition. We compiled existing camera trap data sets from 65 traps into a single data set including more than 3.7 million photos containing images of free-roaming horses from California, Nevada, Utah, and Wyoming. We employed veterinarian-trained animal science students to score horses within randomly sampled, spatially/temporally congruous sequences of animals in our data sets. We compared student scores with veterinarian scores to evaluate whether scorer-specific correction factors are necessary for estimating BCS parameters. Calibration to a fixed reference set reduced between-scorer variance to zero in our population-level BCS estimates. Analysis of our between-scorer variance in our empirical data implies this calibration step can be beneficial when different observers are scoring distinct horse populations across space and/or time.

California, Nevada, Utah, Wyoming↗

USGS2026

The application of state-and-transition models to remotely sensed fractional cover datasets

Assessing vegetation conditions in expansive rangeland ecosystems has long posed a persistent challenge. Recent advancements in remote sensing technologies have provided new avenues for improving this assessment process. In this study, conducted at two ranch sites in Nevada, we integrated existing line-point-intercept monitoring data with additional field observations to evaluate the vegetation condition or “State” at various monitoring points. We then employed machine learning techniques to classify gridded raster datasets, aligning them with existing state-and-transition models (STMs) specific to each study area. Leveraging Landsat-derived fractional cover datasets, along with climate and soil predictors, we aimed to predict vegetation State within specific Vegetation Great Groups (VGGs). The resulting vegetation State maps were then combined to generate a cohesive representation of vegetation conditions across the study sites. Our analysis revealed that relative functional group cover emerged as a superior predictor of vegetation state and ecological processes at the site level. However, we encountered variations in state mapping accuracy ranging from approximately 14% to 44% error. These discrepancies were influenced by factors such as study location, landscape heterogeneity, availability of training data, and species-specific challenges, all of which complicate the accurate classification of remote sensing datasets.

Nevada↗

USGS2016

Evaluating mountain meadow groundwater response to Pinyon-Juniper and temperature in a great basin watershed

This research highlights development and application of an integrated hydrologic model (GSFLOW) to a semiarid, snow-dominated watershed in the Great Basin to evaluate Pinyon-Juniper (PJ) and temperature controls on mountain meadow shallow groundwater. The work used Google Earth Engine Landsat satellite and gridded climate archives for model evaluation. Model simulations across three decades indicated that the watershed operates on a threshold response to precipitation (P) >400 mm/y to produce a positive yield (P-ET; 9%) resulting in stream discharge and a rebound in meadow groundwater levels during these wetter years. Observed and simulated meadow groundwater response to large P correlates with above average predicted soil moisture and with a normalized difference vegetation index threshold value >0.3. A return to assumed pre-expansion PJ conditions or an increase in temperature to mid-21st century shifts yielded by only ±1% during the multi-decade simulation period; but changes of approximately ±4% occurred during wet years. Changes in annual yield were largely dampened by the spatial and temporal redistribution of evapotranspiration across the watershed: Yet the influence of this redistribution and vegetation structural controls on snowmelt altered recharge to control water table depth in the meadow. Even a small-scale removal of PJ (0.5 km 2 ) proximal to the meadow will promote a stable, shallow groundwater system resilient to droughts, while modest increases in temperature will produce a meadow susceptible to declining water levels and a community structure likely to move toward dry and degraded conditions.

Nevada↗