IN SEARCH OF THE FOREST PRIMEVAL: DATA-DRIVEN APPROACHES TO MAPPING HISTORIC VEGETATION

Biogeography is the study of the spatial distribution of biota. It is a comparative and observational science that seeks to describe the variations in the spatial patterns of biodiversity through the examination of historical (e.g. vicariance, speciation, and extinction) and ecological (e.g. climate, edaphic, and topographic) factors. Additionally, researchers are increasingly recognizing the role that anthropogenic disturbance regimes have played in shaping current biogeographic patterns. Indeed, in many parts of the world, humans have become the dominant force in alterations to biotic distributions. Since human activities can influence biotic patterns for many years, the interpretation of biogeographic phenomenon without consideration of human influence may lead to erroneous conclusions.This research is built upon the broad supposition that evaluation of current biogeographic patterns must be predicated on antecedent conditions, typically prior to widespread anthropogenic disturbance regimes. To this end, this research utilizes historical data to create baselines from which subsequent changes in biogeographic patterns can be measured. In a narrow sense, this dissertation focuses on land use, land cover, and woody plant compositional changes in the Arbuckle Mountains of south-central Oklahoma during a period of rapid demographic change (circa 1870 to 1898). In this regard, this research seeks to provide insight into the ecological processes of habitat fragmentation, woody plant encroachment, and mesophication that are believed to have occurred subsequent to the periods under investigation in this research.In a broader context, this research is an evaluation of how anthropogenic alterations in landscape pattern and processes may affect the distributions of individual woody plant taxon. Though the datasets utilized in this research are unique to the region, the methods employed in this study should be transferable to other areas of interest. Additionally, the patterns and processes under investigation are not unique to the region under investigation. The results of this research, therefore, should be placed within the context of anthropogenic change that has occurred throughout the eastern deciduous forests of North America, particularly in the western cross timbers, in the period following European settlement.In order to accomplish these goals, this dissertation is divided into two broad research themes. The first employs repeat Public Land Survey System (PLS) data from the 1870s and 1890s, respectively, to quantify changes in landscape structure, woody taxa assemblages, and anthropogenic markers in the Arbuckle Mountains during this period of rapid demographic transition. The second utilizes a Bayesian method known as weights-of-evidence to address the problem of coarse sampling structure of PLS records. The results of this research indicate that the landscape of the Arbuckle Mountains became increasingly fragmented during the approximately 27 years between the two surveys, primarily due to land clearance for agriculture, transportation networks, and anthropogenic structures. Additionally, there were changes in stand composition between the two surveys, implying that these anthropogenic disturbance regimes may be responsible for shifts in biogeographic patterns. The weights-of-evidence method proved to be a statistically valid method to map individual taxon distributions at finer resolutions than afforded from traditional methods of mapping PLS data. These findings will help further elucidate subsequent distributional shifts in these taxa, thereby providing a better understanding of contemporary biogeographic patterns

Mapping annual forest cover in sub-humid and semi-arid Regions through analysis of Landsat and PALSAR imagery

Accurately mapping the spatial distribution of forests in sub-humid to semi-arid regions over time is important for forest management but a challenging task. Relatively large uncertainties still exist in the spatial distribution of forests and forest changes in the sub-humid and semi-arid regions. Numerous publications have used either optical or synthetic aperture radar (SAR) remote sensing imagery, but the resultant forest cover maps often have large errors. In this study, we propose a pixel- and rule-based algorithm to identify and map annual forests from 2007 to 2010 in Oklahoma, USA, a transitional region with various climates and landscapes, using the integration of the L-band Advanced Land Observation Satellite (ALOS) PALSAR Fine Beam Dual Polarization (FBD) mosaic dataset and Landsat images. The overall accuracy and Kappa coefficient of the PALSAR/Landsat forest map were about 88.2% and 0.75 in 2010, with the user and producer accuracy about 93.4% and 75.7%, based on the 3270 random ground plots collected in 2012 and 2013. Compared with the forest products from Japan Aerospace Exploration Agency (JAXA), National Land Cover Database (NLCD), Oklahoma Ecological Systems Map (OKESM) and Oklahoma Forest Resource Assessment (OKFRA), the PALSAR/Landsat forest map showed great improvement. The area of the PALSAR/Landsat forest was about 40,149 km2 in 2010, which was close to the area from OKFRA (40,468 km2), but much larger than those from JAXA (32,403 km2) and NLCD (37,628 km2). We analyzed annual forest cover dynamics, and the results show extensive forest cover loss (2761 km2, 6.9% of the total forest area in 2010) and gain (3630 km2, 9.0%) in southeast and central Oklahoma, and the total area of forests increased by 684 km2 from 2007 to 2010. This study clearly demonstrates the potential of data fusion between PALSAR and Landsat images for mapping annual forest cover dynamics in sub-humid to semi-arid regions, and the resultant forest maps would be helpful to forest management.This study was supported in part by research grants from the National Institute of Food and Agriculture, U.S. Department of Agriculture (2013-69002), the National Science Foundation (NSF) EPSCoR program (OIA-1301789), and the Department of the Interior, United States Geological Survey to AmericaView (G14AP00002).Ye

Cancer therapy shapes the fitness landscape of clonal hematopoiesis.

Acquired mutations are pervasive across normal tissues. However, understanding of the processes that drive transformation of certain clones to cancer is limited. Here we study this phenomenon in the context of clonal hematopoiesis (CH) and the development of therapy-related myeloid neoplasms (tMNs). We find that mutations are selected differentially based on exposures. Mutations in ASXL1 are enriched in current or former smokers, whereas cancer therapy with radiation, platinum and topoisomerase II inhibitors preferentially selects for mutations in DNA damage response genes (TP53, PPM1D, CHEK2). Sequential sampling provides definitive evidence that DNA damage response clones outcompete other clones when exposed to certain therapies. Among cases in which CH was previously detected, the CH mutation was present at tMN diagnosis. We identify the molecular characteristics of CH that increase risk of tMN. The increasing implementation of clinical sequencing at diagnosis provides an opportunity to identify patients at risk of tMN for prevention strategies

Cancer therapy shapes the fitness landscape of clonal hematopoiesis.

Acquired mutations are pervasive across normal tissues. However, understanding of the processes that drive transformation of certain clones to cancer is limited. Here we study this phenomenon in the context of clonal hematopoiesis (CH) and the development of therapy-related myeloid neoplasms (tMNs). We find that mutations are selected differentially based on exposures. Mutations in ASXL1 are enriched in current or former smokers, whereas cancer therapy with radiation, platinum and topoisomerase II inhibitors preferentially selects for mutations in DNA damage response genes (TP53, PPM1D, CHEK2). Sequential sampling provides definitive evidence that DNA damage response clones outcompete other clones when exposed to certain therapies. Among cases in which CH was previously detected, the CH mutation was present at tMN diagnosis. We identify the molecular characteristics of CH that increase risk of tMN. The increasing implementation of clinical sequencing at diagnosis provides an opportunity to identify patients at risk of tMN for prevention strategies

Cancer therapy shapes the fitness landscape of clonal hematopoiesis

Acquired mutations are pervasive across normal tissues. However, understanding of the processes that drive transformation of certain clones to cancer is limited. Here we study this phenomenon in the context of clonal hematopoiesis (CH) and the development of therapy-related myeloid neoplasms (tMNs). We find that mutations are selected differentially based on exposures. Mutations in ASXL1 are enriched in current or former smokers, whereas cancer therapy with radiation, platinum and topoisomerase II inhibitors preferentially selects for mutations in DNA damage response genes (TP53, PPM1D, CHEK2). Sequential sampling provides definitive evidence that DNA damage response clones outcompete other clones when exposed to certain therapies. Among cases in which CH was previously detected, the CH mutation was present at tMN diagnosis. We identify the molecular characteristics of CH that increase risk of tMN. The increasing implementation of clinical sequencing at diagnosis provides an opportunity to identify patients at risk of tMN for prevention strategies. Environmental exposures shape patterns of selection for mutations in clonal hematopoiesis. Cancer therapies promote the growth of clones with mutations that are strongly enriched in treatment-related myeloid neoplasms

Integrated Genomic Characterization of Papillary Thyroid Carcinoma

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Here, we describe the genomic landscape of 496 PTCs. We observed a low frequency of somatic alterations (relative to other carcinomas) and extended the set of known PTC driver alterations to include EIF1AX, PPM1D, and CHEK2 and diverse gene fusions. These discoveries reduced the fraction of PTC cases with unknown oncogenic driver from 25% to 3.5%. Combined analyses of genomic variants, gene expression, and methylation demonstrated that different driver groups lead to different pathologies with distinct signaling and differentiation characteristics. Similarly, we identified distinct molecular subgroups of BRAF-mutant tumors, and multidimensional analyses highlighted a potential involvement of onco-miRs in less-differentiated subgroups. Our results propose a reclassification of thyroid cancers into molecular subtypes that better reflect their underlying signaling and differentiation properties, which has the potential to improve their pathological classification and better inform the management of the diseaseclose6