DYNAMICS OF THE SHORT TERM AND LONG TERM AVIAN DISTRIBUTIONS IN NORTH AMERICA - THE ROLES OF VEGETATION HEIGHT HETEROGENEITY AND CLIMATIC FACTORS

Understanding how biodiversity spatially distribute over both the short term and long term, and what factors are affecting the distribution, are critical for modeling the spatial pattern of biodiversity as well as for promoting effective conservation planning and practices. This dissertation aims to examine factors that influence short-term and long-term avian distribution from the geographical sciences perspective. The research develops landscape level habitat metrics to characterize forest height heterogeneity and examines their efficacies in modelling avian richness at the continental scale. Two types of novel vegetation-height-structured habitat metrics are created based on second order texture algorithms and the concepts of patch-based habitat metrics. I correlate the height-structured metrics with the richness of different forest guilds, and also examine their efficacies in multivariate richness models. The results suggest that height heterogeneity, beyond canopy height alone, supplements habitat characterization and richness models of two forest bird guilds. The metrics and models derived in this study demonstrate practical examples of utilizing three-dimensional vegetation data for improved characterization of spatial patterns in species richness. The second and the third projects focus on analyzing centroids of avian distributions, and testing hypotheses regarding the direction and speed of these shifts. I first showcase the usefulness of centroids analysis for characterizing the distribution changes of a few case study species. Applying the centroid method on 57 permanent resident bird species, I show that multi-directional distribution shifts occurred in large number of studied species. I also demonstrate, plain birds are not shifting their distribution faster than mountain birds, contrary to the prediction based on climate change velocity hypothesis. By modelling the abundance change rate at regional level, I show that extreme climate events and precipitation measures associate closely with some of the long-term distribution shifts. This dissertation improves our understanding on bird habitat characterization for species richness modelling, and expands our knowledge on how avian populations shifted their ranges in North America responding to changing environments in the past four decades. The results provide an important scientific foundation for more accurate predictive species distribution modeling in future

Environmental Differences between Migratory and Resident Ungulates—Predicting Movement Strategies in Rocky Mountain Mule Deer (Odocoileus hemionus) with Remotely Sensed Plant Phenology, Snow, and Land Cover

Migration is a valuable life history strategy for many species because it enables individuals to exploit spatially and temporally variable resources. Globally, the prevalence of species’ migratory behavior is decreasing as individuals forgo migration to remain resident year-round, an effect hypothesized to result from anthropogenic changes to landscape dynamics. Efforts to conserve and restore migrations require an understanding of the ecological characteristics driving the behavioral tradeoff between migration and residence. We identified migratory and resident behaviors of 42 mule deer (Odocoileus hemionus) based on GPS locations and correlated their locations to remotely sensed indicators of forage quality, land cover, snow cover, and human land use. The model classified mule deer seasonal migratory and resident niches with an overall accuracy of 97.8% and cross-validated accuracy of 81.2%. The distance to development was the most important variable in discriminating in which environments these behaviors occur, with resident niche space most often closer to developed areas than migratory niches. Additionally, snow cover in December was important for discriminating summer migratory niches. This approach demonstrates the utility of niche analysis based on remotely sensed environmental datasets and provides empirical evidence of human land use impacts on large-scale wildlife migrations

Development of a SCAR Marker for Rapid Identification of New Kentucky Bluegrass Breeding Lines

As a commonly used turfgrass, Kentucky bluegrass (Poa pratensis L.) (KBG) has many commercially available cultivars for production. After several years of screening, two new lines were obtained (‘KBG03’ and ‘KBG04’), which have high tolerance to summer. The study showed that the two lines revealed similar morphological characteristics, with light green leaf color, narrow leaf blade, high plant height and light 1,000-grain weight. A total of 400 random amplified polymorphic DNA (RAPD) primers and 256 sequence-related amplified polymorphism (SRAP) primer combinations were screened among the two lines and other 4 imported commercial cultivars. The percentages of polymorphic sites were 65.5% (RAPD) and 22.6% (SRAP) respectively. By cluster analysis of RAPD and SRAP data, the dendrogram at a similarity of 0.29 gave two main clusters, of which one group had 4 commercial cultivars, and the other had the two new breeding lines. Furthermore, one specific band of ‘KBG04’ was successfully converted into a dominant sequence characterized amplified region marker (SCAR196). Then the SCAR marker was verified by 39 KBG DNA samples, including imported varieties, domestic varieties and self-breeding lines of our laboratory, and it exhibited high consistency with the original RAPD polymorphic amplification. The results showed that the SCAR marker can be used to distinguish the new line ‘KBG04’ from numerous KBG germplasms, which would be useful for cultivar identification and property rights protection in the future

The Variation of Nasal Microbiota Caused by Low Levels of Gaseous Ammonia Exposure in Growing Pigs

Exposure to gaseous ammonia, even at low levels, can be harmful to pigs and human health. However, less is known about the effects of sustained exposure to gaseous ammonia on nasal microbiota colonization in growing pigs. A total of 120 Duroc×Landrace×Yorkshire pigs were housed in 24 separate chambers and continuously exposed to gaseous ammonia at 0,5, 10, 15, 20, and 25 ppm (four groups per exposure level) for 4 weeks. Then, we used high-throughput sequencing to perform 16S rRNA gene analysis in nasal swabs samples from 72 pigs (n = 12). The results of the nasal microbiota analysis showed that an increase in ammonia concentration, especially at 20 and 25 ppm, decreased the alpha diversity and relative abundance of nasal microbiota. Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria, and Chloroflexi were the most abundant phyla. In addition, the relative abundances of 24 microbial genera significantly changed as the ammonia level increased. Four microbial genera (Pseudomonas, Lactobacillus, Prevotella, and Bacteroides) were significantly decreased at 25 ppm, while only two genera (Moraxella and Streptococcus) were increased at 25 ppm. PICRUSt analyses showed that the relative abundances of the nasal microbiota involved in cell motility, signal transduction, the nervous system, environmental adaptation, and energy and carbohydrate metabolism were significantly decreased, while genes involved in the immune system, endocrine system, circulatory system, immune system diseases and metabolism of vitamins, lipid, and amino acids were increased with increased ammonia levels. The results of in vivo tests showed that an increase in ammonia levels, especially an ammonia level of 25 ppm, caused respiratory tract injury and increase the number of Moraxella and Streptococcus species, while simultaneously decreasing respiratory immunity and growth performance, consistent with the increased presence of harmful bacteria identified by nasal microbiota analysis. Herein, this study also indicted that the threshold concentration of ammonia in pig farming is 20 ppm

A SARS-CoV-2 protein interaction map reveals targets for drug repurposing

The novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 2.3 million people, killed over 160,000, and caused worldwide social and economic disruption1,2. There are currently no antiviral drugs with proven clinical efficacy, nor are there vaccines for its prevention, and these efforts are hampered by limited knowledge of the molecular details of SARS-CoV-2 infection. To address this, we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), identifying 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (29 FDA-approved drugs, 12 drugs in clinical trials, and 28 preclinical compounds). Screening a subset of these in multiple viral assays identified two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the Sigma1 and Sigma2 receptors. Further studies of these host factor targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19

Global Commodity Markets, Chinese Demand for Maize, and Deforestation in Northern Myanmar

This paper makes a significant contribution to understanding the logic of deforestation in Northern Myanmar and connects global trends and regional political economy with local environmental changes. Methodologically, through a combination of remote sensing GIS analysis, for which we use a newly available Myanmar Forest Change dataset produced by TerraPulse and the Smithsonian Conservation Biology Institute, as well as on-the-ground field research observations and interviews with farmers, this paper examines how the expansion of maize plantations in the northern part of Myanmar has implications for deforestation in the region. It argues that a combination of global commodity price shock around 2011–2012 plus easy market access to China generated strong incentives for local farmers to increase the cultivation of maize. The paper contributes to how we understand the environmental impacts of Chinese demands for agricultural products in Southeast Asia

Modeling Impacts of Climate Change on Giant Panda Habitat

Giant pandas (Ailuropoda melanoleuca) are one of the most widely recognized endangered species globally. Habitat loss and fragmentation are the main threats, and climate change could significantly impact giant panda survival. We integrated giant panda habitat information with general climate models (GCMs) to predict future geographic distribution and fragmentation of giant panda habitat. Results support a major general prediction of climate change—a shift of habitats towards higher elevation and higher latitudes. Our models predict climate change could reduce giant panda habitat by nearly 60% over 70 years. New areas may become suitable outside the current geographic range but much of these areas is far from the current giant panda range and only 15% fall within the current protected area system. Long-term survival of giant pandas will require the creation of new protected areas that are likely to support suitable habitat even if the climate changes

Environmental Differences between Migratory and Resident Ungulates—Predicting Movement Strategies in Rocky Mountain Mule Deer (Odocoileus hemionus) with Remotely Sensed Plant Phenology, Snow, and Land Cover

Migration is a valuable life history strategy for many species because it enables individuals to exploit spatially and temporally variable resources. Globally, the prevalence of species’ migratory behavior is decreasing as individuals forgo migration to remain resident year-round, an effect hypothesized to result from anthropogenic changes to landscape dynamics. Efforts to conserve and restore migrations require an understanding of the ecological characteristics driving the behavioral tradeoff between migration and residence. We identified migratory and resident behaviors of 42 mule deer (Odocoileus hemionus) based on GPS locations and correlated their locations to remotely sensed indicators of forage quality, land cover, snow cover, and human land use. The model classified mule deer seasonal migratory and resident niches with an overall accuracy of 97.8% and cross-validated accuracy of 81.2%. The distance to development was the most important variable in discriminating in which environments these behaviors occur, with resident niche space most often closer to developed areas than migratory niches. Additionally, snow cover in December was important for discriminating summer migratory niches. This approach demonstrates the utility of niche analysis based on remotely sensed environmental datasets and provides empirical evidence of human land use impacts on large-scale wildlife migrations