Conservation Genetics of Exploited Finnish Salmonid Fishes

Genetic diversity is one of the levels of biodiversity that the World Conservation Union (IUCN) has recognized as being important to preserve. This is because genetic diversity is fundamental to the future evolution and to the adaptive flexibility of a species to respond to the inherently dynamic nature of the natural world. Therefore, the key to maintaining biodiversity and healthy ecosystems is to identify, monitor and maintain locally-adapted populations, along with their unique gene pools, upon which future adaptation depends. Thus, conservation genetics deals with the genetic factors that affect extinction risk and the genetic management regimes required to minimize the risk. The conservation of exploited species, such as salmonid fishes, is particularly challenging due to the conflicts between different interest groups. In this thesis, I conduct a series of conservation genetic studies on primarily Finnish populations of two salmonid fish species (European grayling, Thymallus thymallus, and lake-run brown trout, Salmo trutta) which are popular recreational game fishes in Finland. The general aim of these studies was to apply and develop population genetic approaches to assist conservation and sustainable harvest of these populations. The approaches applied included: i) the characterization of population genetic structure at national and local scales; ii) the identification of management units and the prioritization of populations for conservation based on evolutionary forces shaping indigenous gene pools; iii) the detection of population declines and the testing of the assumptions underlying these tests; and iv) the evaluation of the contribution of natural populations to a mixed stock fishery. Based on microsatellite analyses, clear genetic structuring of exploited Finnish grayling and brown trout populations was detected at both national and local scales. Finnish grayling were clustered into three genetically distinct groups, corresponding to northern, Baltic and south-eastern geographic areas of Finland. The genetic differentiation among and within population groups of grayling ranged from moderate to high levels. Such strong genetic structuring combined with low genetic diversity strongly indicates that genetic drift plays a major role in the evolution of grayling populations. Further analyses of European grayling covering the majority of the species’ distribution range indicated a strong global footprint of population decline. Using a coalescent approach the beginning of population reduction was dated back to 1 000-10 000 years ago (ca. 200-2 000 generations). Forward simulations demonstrated that the bottleneck footprints measured using the M ratio can persist within small populations much longer than previously anticipated in the face of low levels of gene flow. In contrast to the M ratio, two alternative methods for genetic bottleneck detection identified recent bottlenecks in six grayling populations that warrant future monitoring. Consistent with the predominant role of random genetic drift, the effective population size (N_e) estimates of all grayling populations were very low with the majority of N_e estimates below 50. Taken together, highly structured local populations, limited gene flow and the small N_e of grayling populations indicates that grayling populations are vulnerable to overexploitation and, hence, monitoring and careful management using the precautionary principles is required not only in Finland but throughout Europe. Population genetic analyses of lake-run brown trout populations in the Inari basin (northernmost Finland) revealed hierarchical population structure where individual populations were clustered into three population groups largely corresponding to different geographic regions of the basin. Similar to my earlier work with European grayling, the genetic differentiation among and within population groups of lake-run brown trout was relatively high. Such strong differentiation indicated that the power to determine the relative contribution of populations in mixed fisheries should be relatively high. Consistent with these expectations, high accuracy and precision in mixed stock analysis (MSA) simulations were observed. Application of MSA to indigenous fish caught in the Inari basin identified altogether twelve populations that contributed significantly to mixed stock fisheries with the Ivalojoki river system being the major contributor (70%) to the total catch. When the contribution of wild trout populations to the fisheries was evaluated regionally, geographically nearby populations were the main contributors to the local catches. MSA also revealed a clear separation between the lower and upper reaches of Ivalojoki river system – in contrast to lower reaches of the Ivalojoki river that contributed considerably to the catch, populations from the upper reaches of the Ivalojoki river system (>140 km from the river mouth) did not contribute significantly to the fishery. This could be related to the available habitat size but also associated with a resident type life history and increased cost of migration. The studies in my thesis highlight the importance of dense sampling and wide population coverage at the scale being studied and also demonstrate the importance of critical evaluation of the underlying assumptions of the population genetic models and methods used. These results have important implications for conservation and sustainable fisheries management of Finnish populations of European grayling and brown trout in the Inari basin.

Development of Russian Ports in The Gulf of Finland

Russia has been one of the fastest developing economic areas in the world. Based on the GDP, the Russian economy grew evenly since the crisis in 1998 up till 2008. The growth in the gross domestic product has annually been some 5–10%. In 2007, the growth reached 8.1%, which is the highest figure after the 10% growth in 2000. Due to the growth of the economy and wage levels, purchasing power and consumption have been strongly increasing. The growing consumption has especially increased the imports of durables, such as passenger cars, domestic appliances and electronics. The Russian ports and infrastructure have not been able to satisfy the growing needs of exports and imports, which is why quite a large share of Russian foreign trade is going through third countries as transit transports. Finnish ports play a major role in transit transports to and from Russia. About 15% of the total value of Russian imports was transported through Finland in 2008. The economic recession that started in autumn 2008 and continues to date has had an impact on the economic development of Russia. The export income has decreased, mainly due to the reduced world market prices of energy products (oil and gas) and raw minerals. Investments have been postponed, getting credit is more difficult than before, and the ruble has weakened in relation to the euro and the dollar. The imports are decreasing remarkably, and are not forecast to reach the 2008 volumes even in 2012. The economic crisis is reflected in Finland's transit traffic. The volume of goods transported through Finland to and from Russia has decreased almost in the same proportion as the imports of goods to Russia. The biggest risk threatening the development of the Russian economy over long term is its dependence on export income from oil, gas, metals, minerals and forest products, as well as the trends of the world market prices of these products. Nevertheless, it is expected that the GDP of Russia will start to grow again in the forthcoming years due to the increased demand for energy products and raw minerals in the world. At the same time, it is obvious that the world market prices of these products will go up with the increasing demand. The increased income from exports will lead to a growth of imports, especially those of consumer goods, as the living standard of Russian citizens rises. The forecasts produced by the Russian Government concerning the economic development of Russia up till 2030 also indicate a shift in exported goods from raw materials to processed products, which together with energy products will become the main export goods of Russia. As a consequence, Russia may need export routes through third countries, which can be seen as an opportunity for increased transit transports through the ports of Finland. The ports competing with the ports of Finland for Russian foreign trade traffic are the Russian Baltic Sea ports and the ports of the Baltic countries. The strongest competitors are the Baltic Sea ports handling containers. On the Russian Baltic Sea, these ports include Saint Petersburg, Kaliningrad and, in the near future, the ports of Ust-Luga and possibly Vyborg. There are plans to develop Ust-Luga and Vyborg as modern container ports, which would become serious competitors to the Finnish ports. Russia is aiming to redirect as large a share as possible of foreign trade traffic to its own ports. The ports of Russia and the infrastructure associated with them are under constant development. On the other hand, the logistic capacity of Russia is not able to satisfy the continually growing needs of the Russian foreign trade. The capacity problem is emphasized by a structural incompatibility between the exports and imports in the Russian foreign trade. Russian exports can only use a small part of the containers brought in with imports. Problems are also caused by the difficult ice conditions and narrow waterways leading to the ports. It is predicted that Finland will maintain its position as a transit route for the Russian foreign trade, at least in the near future. The Russian foreign trade is increasing, and Russia will not be able to develop its ports in proportion with the increasing foreign trade. With the development of port capacity, cargo flows through the ports of Russia will grow. Structural changes in transit traffic are already visible. Firms are more and more relocating their production to Russia, for example as regards the assembly of cars and warehousing services. Simultaneously, an increasing part of transit cargoes are sent directly to Russia without unloading and reloading in Finland. New product groups have nevertheless been transported through Finland (textile products and tools), replacing the lost cargos. The global recession that started in autumn 2008 has influenced the volume of Russian imports and, consequently, the transit volumes of Finland, but the recession is not expected to be of long duration, and will thus only have a short-term impact on transit volumes. The Finnish infrastructure and services offered by the logistic chain should also be ready to react to the changes in imported product groups as well as to the change in Russian export products in the future. If the development plans of the Russian economy are realized, export products will be more refined, and the share of energy and raw material products will decrease. The other notable factor to be taken into consideration is the extremely fast-changing business environment in Russia. Operators in the logistic chain should be flexible enough to adapt to all kinds of changes to capitalise on business opportunities offered by the Russian foreign trade for the companies and for the transit volumes of Finnish ports, also in the future.

Demographic Modelling of Human Population Growth

This work presents models and methods that have been used in producing forecasts of population growth. The work is intended to emphasize the reliability bounds of the model forecasts. Leslie model and various versions of logistic population models are presented. References to literature and several studies are given. A lot of relevant methodology has been developed in biological sciences. The Leslie modelling approach involves the use of current trends in mortality,fertility, migration and emigration. The model treats population divided in age groups and the model is given as a recursive system. Other group of models is based on straightforward extrapolation of census data. Trajectories of simple exponential growth function and logistic models are used to produce the forecast. The work presents the basics of Leslie type modelling and the logistic models, including multi- parameter logistic functions. The latter model is also analysed from model reliability point of view. Bayesian approach and MCMC method are used to create error bounds of the model predictions.

Cuticular and Suberin Polymers of Edible Plants. Analysis by Gas Chromatographic-Mass Spectrometric and Solid State Spectroscopic Methods

Cutin and suberin are structural and protective polymers of plant surfaces. The epidermal cells of the aerial parts of plants are covered with an extracellular cuticular layer, which consists of polyester cutin, highly resistant cutan, cuticular waxes and polysaccharides which link the layer to the epidermal cells. A similar protective layer is formed by a polyaromatic-polyaliphatic biopolymer suberin, which is present particularly in the cell walls of the phellem layer of periderm of the underground parts of plants (e.g. roots and tubers) and the bark of trees. In addition, suberization is also a major factor in wound healing and wound periderm formation regardless of the plants’ tissue. Knowledge of the composition and functions of cuticular and suberin polymers is important for understanding the physiological properties for the plants and for nutritional quality when these plants are consumed as foods. The aims of the practical work were to assess the chemical composition of cuticular polymers of several northern berries and seeds and suberin of two varieties of potatoes. Cutin and suberin were studied as isolated polymers and further after depolymerization as soluble monomers and solid residues. Chemical and enzymatic depolymerization techniques were compared and a new chemical depolymerization method was developed. Gas chromatographic analysis with mass spectrometric detection (GC-MS) was used to assess the monomer compositions. Polymer investigations were conducted with solid state carbon-13 cross polarization magic angle spinning nuclear magnetic resonance spectroscopy (13C CP-MAS NMR), Fourier transform infrared spectroscopy (FTIR) and microscopic analysis. Furthermore, the development of suberin over one year of post-harvest storage was investigated and the cuticular layers from berries grown in the North and South of Finland were compared. The results show that the amounts of isolated cuticular layers and cutin monomers, as well as monomeric compositions vary greatly between the berries. The monomer composition of seeds was found to differ from the corresponding berry peel monomers. The berry cutin monomers were composed mostly of long-chain aliphatic ω-hydroxy acids, with various mid-chain functionalities (double-bonds, epoxy, hydroxy and keto groups). Substituted α,ω-diacids predominated over ω-hydroxy acids in potato suberin monomers and slight differences were found between the varieties. The newly-developed closed tube chemical method was found to be suitable for cutin and suberin analysis and preferred over the solvent-consuming and laborious reflux method. Enzymatic hydrolysis with cutinase was less effective than chemical methanolysis and showed specificity towards α,ω-diacid bonds. According to ₁₃C CP-MAS NMR and FTIR, the depolymerization residues contained significant amounts of aromatic structures, polysaccharides and possible cutan-type aliphatic moieties. Cultivation location seems to have effect on cuticular composition. The materials studied contained significant amounts of different types of biopolymers that could be utilized for several purposes with or without further processing. The importance of the so-called waste material from industrial processes of berries and potatoes as a source of either dietary fiber or specialty chemicals should be further investigated in detail. The evident impact of cuticular and suberin polymers, among other fiber components, on human health should be investigated in clinical trials. These by-product materials may be used as value-added fiber fractions in the food industry and as raw materials for specialty chemicals such as lubricants and emulsifiers, or as building blocks for novel polymers.

Optimal trading of wind power in the short term market

The energy reform, which is happening all over the world, is caused by the common concern of the future of the humankind in our shared planet. In order to keep the effects of the global warming inside of a certain limit, the use of fossil fuels must be reduced. The marginal costs of the renewable sources, RES are quite high, since they are new technology. In order to induce the implementation of RES to the power grid and lower the marginal costs, subsidies were developed in order to make the use of RES more profitable. From the RES perspective the current market is developed to favor conventional generation, which mainly uses fossil fuels. Intermittent generation, like wind power, is penalized in the electricity market since it is intermittent and thus diffi-cult to control. Therefore, the need of regulation and thus the regulation costs to the producer differ, depending on what kind of generation market participant owns. In this thesis it is studied if there is a way for market participant, who has wind power to use the special characteristics of electricity market Nord Pool and thus reach the gap between conventional generation and the intermittent generation only by placing bids to the market. Thus, an optimal bid is introduced, which purpose is to minimize the regulation costs and thus lower the marginal costs of wind power. In order to make real life simulations in Nord Pool, a wind power forecast model was created. The simulations were done in years 2009 and 2010 by using a real wind power data provided by Hyötytuuli, market data from Nord Pool and wind forecast data provided by Finnish Meteorological Institute. The optimal bid needs probability intervals and therefore the methodology to create probability distributions is introduced in this thesis. In the end of the thesis it is shown that the optimal bidding improves the position of wind power producer in the electricity market.

Interdependence of Emerging Eastern European stock markets

One of the main developments in the global economy during the past decades has been the growth of emerging economies. Projections for their long-term growth, changes in the investment climate, corporate transparency and demography point to an increasing role for these emerging economies in the global economy. Today, emerging economies are usually considered as financial markets offering opportunities for high returns, good risk diversification and improved return-to-risk ratios. However, researchers have noted that these advantages may be in decline because of the increasing market integration. Nevertheless, it is likely that certain financial markets and specific sectors will remain partially segmented and somewhat insulated from the global economy for the year to come. This doctoral dissertation investigates several stock markets in Emerging Eastern Europe (EEE), including the ones in Russia, Poland, Hungary, the Czech Republic, Bulgaria and Slovenia. The objective is to analyze the returns and financial risks in these emerging markets from international investor’s point of view. This study also examines the segmentation/integration of these financial markets and the possibilities to diversify and hedge financial risk. The dissertation is divided into two parts. The first includes a review of the theoretical background for the articles and a review of the literature on EEE stock markets. It includes an overview of the methodology and research design applied in the analysis and a summary of articles from the second part of this dissertation and their main findings. The second part consists of four research publications. This work contributes to studies on emerging stock markets in four ways. First, it adds to the body of research on the pricing of risk, providing new empirical evidence about partial stock market segmentation in EEE. The results suggest that the aggregate emerging market risk is a relevant driver for stock market returns and that this market risk can be used to price financial instruments and forecast their performance. Second, it contributes to the empirical research on the integration of stock markets, asset prices and exchange rates by identifying the relationships between these markets through volatility and asset pricing. The results show that certain sectors of stock markets in EEE are not as integrated as others. For example, the Polish consumer goods sector, the Hungarian telecommunications sector, and the Czech financial sector are somewhat isolated from their counterparts elsewhere in Europe. Nevertheless, an analysis of the impact of EU accession in 2004 on stock markets suggests that most of the EEE markets are becoming increasingly integrated with the global markets. Third, this thesis complements the scientific literature in the field of shock and volatility spillovers by examining the mechanism of spillover distribution among the EU and EEE countries. The results illustrate that spillovers in emerging markets are mostly from a foreign exchange to the stock markets. Moreover, the results show that the effects of external shocks on stock markets have increased after the enlargement of the EU in 2004. Finally, this study is unique because it analyzes the effects of foreign macroeconomic news on geographically closely related countries. The results suggest that the effects of macroeconomic announcements on volatility are significant and have effect that varies across markets and their sectors. Moreover, the results show that the foreign macroeconomic news releases, somewhat surprisingly, have a greater effect on the EEE markets than the local macroeconomic news. This dissertation has a number of implications for the industry and for practitioners. It analyses financial risk associated with investing in Emerging Eastern Europe. Investors may use this information to construct and optimize investment portfolios. Moreover, this dissertation provides insights for investors and portfolio managers considering asset allocation to protect value or obtain higher returns. The results have also implications for asset pricing and portfolio selection in light of macroeconomic news releases.

Insiders´ views as a predictor of stock performance : can insider trading be used to predict stock performance on the United States stock market?

This thesis studies the possibility of using information on insiders’ transactions to forecast future stock returns after the implementation of Sarbanes Oxley Act in July 2003. Insider transactions between July 2003 and August 2009 are analysed with regression tests to identify the relationships between insiders’ transactions and future stock returns. This analysis is complemented with rudimentary bootstrapping procedures to verify the robustness of the findings. The underlying assumption of the thesis is that insiders constantly receive pieces of information that indicate future performance of the company. They may not be allowed to trade on large and tangible pieces of information but they can trade on accumulation of smaller, intangible pieces of information. Based on the analysis in the thesis insiders’ profits were found not to differ from the returns from broad stock index. However, their individual transactions were found to be linked to future stock returns. The initial model was found to be unstable but some of the predictive power could be sacrificed to achieve greater stability. Even after sacrificing some predictive power the relationship was significant enough to allow external investors to achieve abnormal profits after transaction costs and taxes. The thesis does not go into great detail about timing of transactions. Delay in publishing insiders’ transactions is not taken into account in the calculations and the closed windows are not studied in detail. The potential effects of these phenomena are looked into and they do not cause great changes in the findings. Additionally the remuneration policy of an insider or a company is not taken into account even though it most likely affects the trading patterns of insiders. Even with the limitations the findings offer promising opportunities for investors to improve their investment processes by incorporating additional information from insiders’ transaction into their decisions. The findings also raise questions on how insider trading should be regulated. Insiders achieve greater returns than other investors based on superior information. On the other hand, more efficient information transfer could warrant more lenient regulation. The fact that insiders’ returns are dominated by the large investment stake they maintain all the time in their own companies also speaks for more leniency. As Sarbanes Oxley Act considerably modified the insider trading landscape, this analysis provides information that has not been available before. The thesis also constitutes a thorough analysis of insider trading phenomenon which has previously been somewhat separated into several studies.

Forecasting of wind speeds and directions with artificial neural networks

In this master’s thesis, wind speeds and directions were modeled with the aim of developing suitable models for hourly, daily, weekly and monthly forecasting. Artificial Neural Networks implemented in MATLAB software were used to perform the forecasts. Three main types of artificial neural network were built, namely: Feed forward neural networks, Jordan Elman neural networks and Cascade forward neural networks. Four sub models of each of these neural networks were also built, corresponding to the four forecast horizons, for both wind speeds and directions. A single neural network topology was used for each of the forecast horizons, regardless of the model type. All the models were then trained with real data of wind speeds and directions collected over a period of two years in the municipal region of Puumala in Finland. Only 70% of the data was used for training, validation and testing of the models, while the second last 15% of the data was presented to the trained models for verification. The model outputs were then compared to the last 15% of the original data, by measuring the mean square errors and sum square errors between them. Based on the results, the feed forward networks returned the lowest generalization errors for hourly, weekly and monthly forecasts of wind speeds; Jordan Elman networks returned the lowest errors when used for forecasting of daily wind speeds. Cascade forward networks gave the lowest errors when used for forecasting daily, weekly and monthly wind directions; Jordan Elman networks returned the lowest errors when used for hourly forecasting. The errors were relatively low during training of the models, but shot up upon simulation with new inputs. In addition, a combination of hyperbolic tangent transfer functions for both hidden and output layers returned better results compared to other combinations of transfer functions. In general, wind speeds were more predictable as compared to wind directions, opening up opportunities for further research into building better models for wind direction forecasting.

In For a Penny, In For a Pound: The Management and Preservation of E-Theses in EPrints

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Energy and Dynamics of the solar agricultural robot "SolarSprayer"

The main objective of the present study was to design an agricultural robot, which work is based on the generation of the electricity by the solar panel. To achieve the proper operation of the robot according to the assumed working cycle the detailed design of the main equipment was made. By analysing the possible areas of implementation together with developments, the economic forecast was held. As a result a decision about possibility of such device working in agricultural sector was made and the probable topics of the further study were found out.

Characteristic Approach to Predicting Analysts’ Forecast Errors in the Finnish Market

Extensive literature shows that analysts’ forecasts and recommendations are often biased. Thus, it is important for the financial market to be able to recognize this bias to be able to correctly valuate public companies. This thesis uses characteristic approach, which was introduced by So (2013, pp. 615-640), to forecast analysts’ forecast errors and tests if predictable forecast error is fully incorporated into share prices. Data is collected of listed Finnish companies. Thesis’ timeframe spans over ten years from 2004 to 2013 consisting of 788 firm-years. Although there is earlier evidence that the characteristic approach is able to predict analysts’ forecast errors, no support for this is found in the Finnish market. This thesis contributes to the current knowledge by showing that the characteristic approach does not work universally as such but requires development to work especially in the smaller markets.

Accuracy improvement of a demand forecasting model

This research concerns different statistical methods that assist to increase the demand forecasting accuracy of company X’s forecasting model. Current forecasting process was analyzed in details. As a result, graphical scheme of logical algorithm was developed. Based on the analysis of the algorithm and forecasting errors, all the potential directions for model future improvements in context of its accuracy were gathered into the complete list. Three improvement directions were chosen for further practical research, on their basis, three test models were created and verified. Novelty of this work lies in the methodological approach of the original analysis of the model, which identified its critical points, as well as the uniqueness of the developed test models. Results of the study formed the basis of the grant of the Government of St. Petersburg.

Evaluating and improving demand forecast practices for B2B and B2C sectors

Demand forecasting is one of the fundamental managerial tasks. Most companies do not know their future demands, so they have to make plans based on demand forecasts. The literature offers many methods and approaches for producing forecasts. Former literature points out that even though many forecasting methods and approaches are available, selecting a suitable approach and implementing and managing it is a complex cross-functional matter. However, it’s relatively rare that researches are focused on the differences in forecasting between consumer and industrial companies. The aim of this thesis is to investigate the potential of improving demand forecasting practices for B2B and B2C sectors in the global supply chains. Business to business (B2B) sector produces products for other manufacturing companies. On the other hand, consumer (B2C) sector provides goods for individual buyers. Usually industrial sector have a lower number of customers and closer relationships with them. The research questions of this thesis are: 1) What are the main differences and similarities in demand planning between B2B and B2C sectors? 2) How the forecast performance for industrial and consumer companies can be improved? The main methodological approach in this study is design science, where the main objective is to develop tentative solutions to real-life problems. The research data has been collected from a case company. Evaluation and improving in organizing demand forecasting can be found in three interlinked areas: 1) demand planning operational environment, 2) demand forecasting techniques, 3) demand information sharing scenarios. In this research current B2B and B2C demand practices are presented with further comparison between those two sectors. It was found that B2B and B2C sectors have significant differences in demand practices. This research partly filled the theoretical gap in understanding the difference in forecasting in consumer and industrial sectors. In all these areas, examples of managerial problems are described, and approaches for mitigating these problems are outlined.

Dynamic linkages of real estate and stock markets in Finland

Traditionally real estate has been seen as a good diversification tool for a stock portfolio due to the lower return and volatility characteristics of real estate investments. However, the diversification benefits of a multi-asset portfolio depend on how the different asset classes co-move in the short- and long-run. As the asset classes are affected by the same macroeconomic factors, interrelationships limiting the diversification benefits could exist. This master’s thesis aims to identify such dynamic linkages in the Finnish real estate and stock markets. The results are beneficial for portfolio optimization tasks as well as for policy-making. The real estate industry can be divided into direct and securitized markets. In this thesis the direct market is depicted by the Finnish housing market index. The securitized market is proxied by the Finnish all-sectors securitized real estate index and by a European residential Real Estate Investment Trust index. The stock market is depicted by OMX Helsinki Cap index. Several macroeconomic variables are incorporated as well. The methodology of this thesis is based on the Vector Autoregressive (VAR) models. The long-run dynamic linkages are studied with Johansen’s cointegration tests and the short-run interrelationships are examined with Granger-causality tests. In addition, impulse response functions and forecast error variance decomposition analyses are used for robustness checks. The results show that long-run co-movement, or cointegration, did not exist between the housing and stock markets during the sample period. This indicates diversification benefits in the long-run. However, cointegration between the stock and securitized real estate markets was identified. This indicates limited diversification benefits and shows that the listed real estate market in Finland is not matured enough to be considered a separate market from the general stock market. Moreover, while securitized real estate was shown to cointegrate with the housing market in the long-run, the two markets are still too different in their characteristics to be used as substitutes in a multi-asset portfolio. This implies that the capital intensiveness of housing investments cannot be circumvented by investing in securitized real estate.