GENETIC VARIATION, LOCAL ADAPTATION AND POPULATION STRUCTURE IN NORTH AMERICAN RED OAK SPECIES, QUERCUS RUBRA L. AND Q. ELLIPSOIDALIS E. J. HILL

Forest trees, like oaks, rely on high levels of genetic variation to adapt to varying environmental conditions. Thus, genetic variation and its distribution are important for the long-term survival and adaptability of oak populations. Climate change is projected to lead to increased drought and fire events as well as a northward migration of tree species, including oaks. Additionally, decline in oak regeneration has become increasingly concerning since it may lead to decreased gene flow and increased inbreeding levels. This will in turn lead to lowered levels of genetic diversity, negatively affecting the growth and survival of populations. At the same time, populations at the species’ distribution edge, like those in this study, could possess important stores of genetic diversity and adaptive potential, while also being vulnerable to climatic or anthropogenic changes. A survey of the level and distribution of genetic variation and identification of potentially adaptive genes is needed since adaptive genetic variation is essential for their long-term survival. Oaks possess a remarkable characteristic in that they maintain their species identity and specific environmental adaptations despite their propensity to hybridize. Thus, in the face of interspecific gene flow, some areas of the genome remain differentiated due to selection. This characteristic allows the study of local environmental adaptation through genetic variation analyses. Furthermore, using genic markers with known putative functions makes it possible to link those differentiated markers to potential adaptive traits (e.g., flowering time, drought stress tolerance). Demographic processes like gene flow and genetic drift also play an important role in how genes (including adaptive genes) are maintained or spread. These processes are influenced by disturbances, both natural and anthropogenic. An examination of how genetic variation is geographically distributed can display how these genetic processes and geographical disturbances influence genetic variation patterns. For example, the spatial clustering of closely related trees could promote inbreeding with associated negative effects (inbreeding depression), if gene flow is limited. In turn this can have negative consequences for a species’ ability to adapt to changing environmental conditions. In contrast, interspecific hybridization may also allow the transfer of genes between species that increase their adaptive potential in a changing environment. I have studied the ecologically divergent, interfertile red oaks, Quercus rubra and Q. ellipsoidalis, to identify genes with potential roles in adaptation to abiotic stress through traits such as drought tolerance and flowering time, and to assess the level and distribution of genetic variation. I found evidence for moderate gene flow between the two species and low interspecific genetic differences at most genetic markers (Lind and Gailing 2013). However, the screening of genic markers with potential roles in phenology and drought tolerance led to the identification of a CONSTANS-like (COL) gene, a candidate gene for flowering time and growth. This marker, located in the coding region of the gene, was highly differentiated between the two species in multiple geographical areas, despite interspecific gene flow, and may play a role in reproductive isolation and adaptive divergence between the two species (Lind-Riehl et al. 2014). Since climate change could result in a northward migration of trees species like oaks, this gene could be important in maintaining species identity despite increased contact zones between species (e.g., increased gene flow). Finally I examined differences in spatial genetic structure (SGS) and genetic variation between species and populations subjected to different management strategies and natural disturbances. Diverse management activities combined with various natural disturbances as well as species specific life history traits influenced SGS patterns and inbreeding levels (Lind-Riehl and Gailing submitted).

Beech bark disease distribution and resistance in Michigan and fungal endophyte ecology of resistant and susceptible beech (Fagus grandifolia Ehrh.)

Beech bark disease (BBD), a non-native association of the fungal pathogen Neonectria faginata and the beech scale insect Cryptococcus fagisuga, has dramatically affected American beech within North American forests. To monitor the spread and effects of BBD in Michigan, a network of forest health monitoring plots was established in 2001 following the disease discovery in Ludington State Park (Mason County). Forest health canopy condition and basic forestry measurements including basal area were reassessed on beech trees in these plots in 2011 and 2012. The influence of bark-inhabiting fungal endophytes on BBD resistance was investigated by collecting cambium tissue from apparently resistant and susceptible beech. Vigor rating showed significant influences of BBD in sample beech resulting in reduced health and substantiated by significant increases of dead beech basal area over time. C. fagisuga distribution was found to be spatially clustered and widespread in the 22 counties in Michigan's Lower Peninsula which contained monitoring plots. Neonectria has been found in Emmet, Cheboygan and Wexford in the Lower Peninsula which may coincide with additional BBD introduction locations. Surveys for BBD resistance resulted in five apparently resistant beech which were added to a BBD resistance database. The most frequently isolated endophytes from cambium tissue were identified by DNA sequencing primarily as Deuteromycetes and Ascomycetes including Chaetomium globosum, Neohendersonia kickxii and Fusarium flocciferum. N. faginata in antagonism trials showed significant growth reduction when paired with three beech fungal endophytes. The results of the antagonism trial and decay tests indicate that N. faginata may be a relatively poor competitor in vivo with limited ability to degrade cellulose.

Carbon in the Peatlands in the Great Lakes Region

Peatlands cover only ~3% of the global land area, but store ~30% of the worlds' soil carbon. There are many different peat types that store different amounts of carbon. Most inventories of carbon storage in northern peatlands have been conducted in the expansive Sphagnum dominated peatlands. Although, northern white cedar peatlands (NW cedar, Thuja occidentalis L.) are also one of the most common peatland types in the Great Lakes Region, occupying more than 2 million hectares. NW cedar swamps are understudied, due in part to the difficulties in collection methods. General lack of rapid and consistent sampling methods has also contributed in a lack of carbon stock quantification for many peatlands. The main objective of this thesis is to quantify: 1) to evaluate peat sampling methods 2) the amount of C-stored and the rates of long-term carbon accumulation in NW cedar peatlands. We sampled 38 peatlands separated into four categories (black ash, NW cedar swamp, sedge, and Sphagnum) during the summers of 2011/2012 across northern MN and the Upper Peninsula of MI. Basal dates of peat indicate that cedar peatlands were between 1970-7790 years old. Cedar peatlands are generally shallower than Sphagnum peat, but due to their higher bulk density, hold similar amounts of carbon with our sites averaging ~800 MgC ha-1. We estimate that NW cedar peatlands store over 1.7 Gt of carbon in the Great Lakes Region. Each of the six methods evaluated had a different level of accuracy and requires varying levels of effort and resources. The depth only method and intermittent sampling method were the most accurate methods of peatland sampling.

Price risk management in the copper market using commodity derivatives and options strategies

Metals price risk management is a key issue related to financial risk in metal markets because of uncertainty of commodity price fluctuation, exchange rate, interest rate changes and huge price risk either to metals’ producers or consumers. Thus, it has been taken into account by all participants in metal markets including metals’ producers, consumers, merchants, banks, investment funds, speculators, traders and so on. Managing price risk provides stable income for both metals’ producers and consumers, so it increases the chance that a firm will invest in attractive projects. The purpose of this research is to evaluate risk management strategies in the copper market. The main tools and strategies of price risk management are hedging and other derivatives such as futures contracts, swaps and options contracts. Hedging is a transaction designed to reduce or eliminate price risk. Derivatives are financial instruments, whose returns are derived from other financial instruments and they are commonly used for managing financial risks. Although derivatives have been around in some form for centuries, their growth has accelerated rapidly during the last 20 years. Nowadays, they are widely used by financial institutions, corporations, professional investors, and individuals. This project is focused on the over-the-counter (OTC) market and its products such as exotic options, particularly Asian options. The first part of the project is a description of basic derivatives and risk management strategies. In addition, this part discusses basic concepts of spot and futures (forward) markets, benefits and costs of risk management and risks and rewards of positions in the derivative markets. The second part considers valuations of commodity derivatives. In this part, the options pricing model DerivaGem is applied to Asian call and put options on London Metal Exchange (LME) copper because it is important to understand how Asian options are valued and to compare theoretical values of the options with their market observed values. Predicting future trends of copper prices is important and would be essential to manage market price risk successfully. Therefore, the third part is a discussion about econometric commodity models. Based on this literature review, the fourth part of the project reports the construction and testing of an econometric model designed to forecast the monthly average price of copper on the LME. More specifically, this part aims at showing how LME copper prices can be explained by means of a simultaneous equation structural model (two-stage least squares regression) connecting supply and demand variables. A simultaneous econometric model for the copper industry is built: {█(Q_t^D=e^((-5.0485))∙P_((t-1))^((-0.1868) )∙〖GDP〗_t^((1.7151) )∙e^((0.0158)∙〖IP〗_t ) @Q_t^S=e^((-3.0785))∙P_((t-1))^((0.5960))∙T_t^((0.1408))∙P_(OIL(t))^((-0.1559))∙〖USDI〗_t^((1.2432))∙〖LIBOR〗_((t-6))^((-0.0561))@Q_t^D=Q_t^S )┤ P_((t-1))^CU=e^((-2.5165))∙〖GDP〗_t^((2.1910))∙e^((0.0202)∙〖IP〗_t )∙T_t^((-0.1799))∙P_(OIL(t))^((0.1991))∙〖USDI〗_t^((-1.5881))∙〖LIBOR〗_((t-6))^((0.0717) Where, Q_t^D and Q_t^Sare world demand for and supply of copper at time t respectively. P(t-1) is the lagged price of copper, which is the focus of the analysis in this part. GDPt is world gross domestic product at time t, which represents aggregate economic activity. In addition, industrial production should be considered here, so the global industrial production growth that is noted as IPt is included in the model. Tt is the time variable, which is a useful proxy for technological change. A proxy variable for the cost of energy in producing copper is the price of oil at time t, which is noted as POIL(t ) . USDIt is the U.S. dollar index variable at time t, which is an important variable for explaining the copper supply and copper prices. At last, LIBOR(t-6) is the 6-month lagged 1-year London Inter bank offering rate of interest. Although, the model can be applicable for different base metals' industries, the omitted exogenous variables such as the price of substitute or a combined variable related to the price of substitutes have not been considered in this study. Based on this econometric model and using a Monte-Carlo simulation analysis, the probabilities that the monthly average copper prices in 2006 and 2007 will be greater than specific strike price of an option are defined. The final part evaluates risk management strategies including options strategies, metal swaps and simple options in relation to the simulation results. The basic options strategies such as bull spreads, bear spreads and butterfly spreads, which are created by using both call and put options in 2006 and 2007 are evaluated. Consequently, each risk management strategy in 2006 and 2007 is analyzed based on the day of data and the price prediction model. As a result, applications stemming from this project include valuing Asian options, developing a copper price prediction model, forecasting and planning, and decision making for price risk management in the copper market.

Afghanistan connection : heroin production, distribution, and consumption

Heroin prices are a reflection of supply and demand, and similar to any other market, profits motivate participation. The intent of this research is to examine the change in Afghan opium production due to political conflict affecting Europe’s heroin market and government policies. If the Taliban remain in power, or a new Afghan government is formed, the changes will affect the heroin market in Europe to a certain degree. In the heroin market, the degree of change is dependent on many socioeconomic forces such as law enforcement, corruption, and proximity to Afghanistan. An econometric model that examines the degree of these socioeconomic effects has not been applied to the heroin trade in Afghanistan before. This research uses a two-stage least squares econometric model to reveal the supply and demand of heroin in 36 different countries from the Middle East to Western Europe in 2008. An application of the two-stage least squares model to the heroin market in Europe will attempt to predict the socioeconomic consequences of Afghanistan opium production.