Master of Science

thesisWetlands, known as ciénegas, were once found throughout the basin and range physiographic province in southeastern Arizona, southwestern New Mexico, northern Sonora, and northwestern Chihuahua. These groundwater-dependent wetlands are now largely desiccated due to overgrazing, groundwater depletion, and the resulting incision of streams and rivers. This study consists of four components. First, a more complete inventory of the location of historical ciénegas is compiled. This is accomplished using peer-reviewed journal articles, historical maps, journals from explorers and pioneers, and USGS topographic maps. Second, the geographic extent of the documented ciénegas is hand-digitized through photo interpretation of aerial imagery. Each ciénega is divided by its activity status and the land cover succession path it followed. Third, the zonal statistics for a suite of Landsat Thematic Mapper (TM)-derived indices and elevation derivatives such as slope and aspect are compiled. Geospatial datasets that exhibit low variability among different succession paths are further analyzed as being possible predictor variables for the status or land cover type succession path of a ciénega. The two best predictor variables are the normalized burn ratio (NBR) and the thermal infrared (TIR) band. These two variables are used in a classification tree model to determine the location of other undocumented areas that are likely active ciénegas. The fourth component involves monitoring the trends of inactive, as well as active, ciénegas over the past 25 years using the two most sensitive predictor variables. The interannual variability is indicative of changes in vegetation cover, as well as the degree of saturation of the soil. This component indicates that inactive and active ciénegas have experienced relatively uniform patterns of change that are highly correlated with annual precipitation patterns of the study region. This study results in a more complete inventory of the location and geographic extent of historical ciénegas, as well as a better understanding of the variables that can be used to identify different succession paths of ciénegas and possible undocumented active ciénegas. Additionally, for the first time, the recent trends of change for documented ciénegas have been analyzed using remote sensing techniques. This study serves as a preliminary inventory in what should be a series of studies to gain an understanding of the historical and paleodynamics of ciénegas, as well as possible restoration opportunities across a larger geographic area

Mechanism of age-dependent susceptibility and novel treatment strategy in glutaric acidemia type I

Glutaric acidemia type I (GA-I) is an inherited disorder of lysine and tryptophan metabolism presenting with striatal lesions anatomically and symptomatically similar to Huntington disease. Affected children commonly suffer acute brain injury in the context of a catabolic state associated with nonspecific illness. The mechanisms underlying injury and age-dependent susceptibility have been unknown, and lack of a diagnostic marker heralding brain injury has impeded intervention efforts. Using a mouse model of GA-I, we show that pathologic events began in the neuronal compartment while enhanced lysine accumulation in the immature brain allowed increased glutaric acid production resulting in age-dependent injury. Glutamate and GABA depletion correlated with brain glutaric acid accumulation and could be monitored in vivo by proton nuclear magnetic resonance (1H NMR) spectroscopy as a diagnostic marker. Blocking brain lysine uptake reduced glutaric acid levels and brain injury. These findings provide what we believe are new monitoring and treatment strategies that may translate for use in human GA-I

The tree cover and temperature disparity in US urbanized areas: Quantifying the association with income across 5,723 communities

Urban tree cover provides benefits to human health and well-being, but previous studies suggest that tree cover is often inequitably distributed. Here, we use National Agriculture Imagery Program digital ortho photographs to survey the tree cover inequality for Census blocks in US large urbanized areas, home to 167 million people across 5,723 municipalities and other Census-designated places. We compared tree cover to summer land surface temperature, as measured using Landsat imagery. In 92% of the urbanized areas surveyed, low-income blocks have less tree cover than high-income blocks. On average, low-income blocks have 15.2% less tree cover and are 1.5˚C hotter than high-income blocks. The greatest difference between low- and high-income blocks was found in urbanized areas in the Northeast of the United States, where low-income blocks in some urbanized areas have 30% less tree cover and are 4.0˚C hotter. Even after controlling for population density and built-up intensity, the positive association between income and tree cover is significant, as is the positive association between proportion non-Hispanic white and tree cover. We estimate, after controlling for population density, that low-income blocks have 62 million fewer trees than high-income blocks, equal to a compensatory value of $56 billion ($1,349/person). An investment in tree planting and natural regeneration of $17.6 billion would be needed to close the tree cover disparity, benefitting 42 million people in low-income blocks

An Evaluation of Forest Health Insect and Disease Survey Data and Satellite-Based Remote Sensing Forest Change Detection Methods: Case Studies in the United States

The Operational Remote Sensing (ORS) program leverages Landsat and MODIS data to detect forest disturbances across the conterminous United States (CONUS). The ORS program was initiated in 2014 as a collaboration between the US Department of Agriculture Forest Service Geospatial Technology and Applications Center (GTAC) and the Forest Health Assessment and Applied Sciences Team (FHAAST). The goal of the ORS program is to supplement the Insect and Disease Survey (IDS) and MODIS Real-Time Forest Disturbance (RTFD) programs with imagery-derived forest disturbance data that can be used to augment traditional IDS data. We developed three algorithms and produced ORS forest change products using both Landsat and MODIS data. These were assessed over Southern New England and the Rio Grande National Forest. Reference data were acquired using TimeSync to conduct an independent accuracy assessment of IDS, RTFD, and ORS products. Overall accuracy for all products ranged from 71.63% to 92.55% in the Southern New England study area and 63.48% to 79.13% in the Rio Grande National Forest study area. While the accuracies attained from the assessed products are somewhat low, these results are similar to comparable studies. Although many ORS products met or exceeded the overall accuracy of IDS and RTFD products, the differences were largely statistically insignificant at the 95% confidence interval. This demonstrates the current implementation of ORS is sufficient to provide data to augment IDS data

An Evaluation of Forest Health Insect and Disease Survey Data and Satellite-Based Remote Sensing Forest Change Detection Methods: Case Studies in the United States

The Operational Remote Sensing (ORS) program leverages Landsat and MODIS data to detect forest disturbances across the conterminous United States (CONUS). The ORS program was initiated in 2014 as a collaboration between the US Department of Agriculture Forest Service Geospatial Technology and Applications Center (GTAC) and the Forest Health Assessment and Applied Sciences Team (FHAAST). The goal of the ORS program is to supplement the Insect and Disease Survey (IDS) and MODIS Real-Time Forest Disturbance (RTFD) programs with imagery-derived forest disturbance data that can be used to augment traditional IDS data. We developed three algorithms and produced ORS forest change products using both Landsat and MODIS data. These were assessed over Southern New England and the Rio Grande National Forest. Reference data were acquired using TimeSync to conduct an independent accuracy assessment of IDS, RTFD, and ORS products. Overall accuracy for all products ranged from 71.63% to 92.55% in the Southern New England study area and 63.48% to 79.13% in the Rio Grande National Forest study area. While the accuracies attained from the assessed products are somewhat low, these results are similar to comparable studies. Although many ORS products met or exceeded the overall accuracy of IDS and RTFD products, the differences were largely statistically insignificant at the 95% confidence interval. This demonstrates the current implementation of ORS is sufficient to provide data to augment IDS data

Targeting H3K4 trimethylation in Huntington disease.

Transcriptional dysregulation is an early feature of Huntington disease (HD). We observed gene-specific changes in histone H3 lysine 4 trimethylation (H3K4me3) at transcriptionally repressed promoters in R6/2 mouse and human HD brain. Genome-wide analysis showed a chromatin signature for this mark. Reducing the levels of the H3K4 demethylase SMCX/Jarid1c in primary neurons reversed down-regulation of key neuronal genes caused by mutant Huntingtin expression. Finally, reduction of SMCX/Jarid1c in primary neurons from BACHD mice or the single Jarid1 in a Drosophila HD model was protective. Therefore, targeting this epigenetic signature may be an effective strategy to ameliorate the consequences of HD