Analysis of Atmospheric Particle Formation Events

Atmospheric aerosol particle formation events can be a significant source for tropospheric aerosols and thus influence the radiative properties and cloud cover of the atmosphere. This thesis investigates the analysis of aerosol size distribution data containing particle formation events, describes the methodology of the analysis and presents time series data measured inside the Boreal forest. This thesis presents a methodology to identify regional-scale particle formation, and to derive the basic characteristics such as growth and formation rates. The methodology can also be used to estimate concentration and source rates of the vapour causing particle growth. Particle formation was found to occur frequently in the boreal forest area over areas covering up to hundreds of kilometers. Particle formation rates of boreal events were found to be of the order of 0.01-5 cm^-3 s^-1, while the nucleation rates of 1 nm particles can be a few orders of magnitude higher. The growth rates of over 3 nm sized particles were of the order of a few nanometers per hour. The vapor concentration needed to sustain such growth is of the order of 10^7--10^8 cm^-3, approximately one order of magnitude higher than sulphuric acid concentrations found in the atmosphere. Therefore, one has to assume that other vapours, such as organics, have a key role in growing newborn particles to sizes where they can become climatically active. Formation event occurrence shows a clear annual variation with peaks in summer and autumns. This variation is similar to the variation exhibited the obtained formation rates of particles. The growth rate, on the other hand, reaches its highest values during summer. This difference in the annual behavior, and the fact that no coupling between the growth and formation process could be identified, suggest that these processes might be different ones, and that both are needed for a particle formation burst to be observed.

Hyvinvointivaltion optimaalisen köyhyyspolitiikan määrittelyä

The purpose of this research is to identify the optimal poverty policy for a welfare state. Poverty is defined by income. Policies for reducing poverty are considered primary, and those for reducing inequality secondary. Poverty is seen as a function of the income transfer system within a welfare state. This research presents a method for optimising this function for the purposes of reducing poverty. It is also implemented in the representative population sample within the Income Distribution Data. SOMA simulation model is used. The iterative simulation process is continued until a level of poverty is reached at which improvements can no longer be made. Expenditures and taxes are kept in balance during the process. The result consists of two programmes. The first programme (social assistance programme) was formulated using five social assistance parameters, all of which dealt with the norms of social assistance for adults (€/month). In the second programme (basic benefits programme), in which social assistance was frozen at the legislative level of 2003, the parameter with the strongest poverty reduction effect turned out to be one of the basic unemployment allowances. This was followed by the norm of the national pension for a single person, two parameters related to housing allowance, and the norm for financial aid for students of higher education institutions. The most effective financing parameter measured by gini-coefficient in all programmes was the percent of capital taxation. Furthermore, these programmes can also be examined in relation to their costs. The social assistance programme is significantly cheaper than the basic benefits programme, and therefore with regard to poverty, the social assistance programme is more cost effective than the basic benefits programme. Therefore, public demand for raising the level of basic benefits does not seem to correspond to the most cost effective poverty policy. Raising basic benefits has most effect on reducing poverty within the group of people whose basic benefits are raised. Raising social assistance, on the other hand, seems to have a strong influence on the poverty of all population groups. The most significant outcome of this research is the development of a method through which a welfare state’s income transfer-based safety net, which has severely deteriorated in recent decades, might be mended. The only way of doing so involves either social assistance or some forms of basic benefits and supplementing these by modifying social assistance.

Geospatial environmental data modelling applications using remote sensing, GIS and spatial statistics

This thesis presents novel modelling applications for environmental geospatial data using remote sensing, GIS and statistical modelling techniques. The studied themes can be classified into four main themes: (i) to develop advanced geospatial databases. Paper (I) demonstrates the creation of a geospatial database for the Glanville fritillary butterfly (Melitaea cinxia) in the Åland Islands, south-western Finland; (ii) to analyse species diversity and distribution using GIS techniques. Paper (II) presents a diversity and geographical distribution analysis for Scopulini moths at a world-wide scale; (iii) to study spatiotemporal forest cover change. Paper (III) presents a study of exotic and indigenous tree cover change detection in Taita Hills Kenya using airborne imagery and GIS analysis techniques; (iv) to explore predictive modelling techniques using geospatial data. In Paper (IV) human population occurrence and abundance in the Taita Hills highlands was predicted using the generalized additive modelling (GAM) technique. Paper (V) presents techniques to enhance fire prediction and burned area estimation at a regional scale in East Caprivi Namibia. Paper (VI) compares eight state-of-the-art predictive modelling methods to improve fire prediction, burned area estimation and fire risk mapping in East Caprivi Namibia. The results in Paper (I) showed that geospatial data can be managed effectively using advanced relational database management systems. Metapopulation data for Melitaea cinxia butterfly was successfully combined with GPS-delimited habitat patch information and climatic data. Using the geospatial database, spatial analyses were successfully conducted at habitat patch level or at more coarse analysis scales. Moreover, this study showed it appears evident that at a large-scale spatially correlated weather conditions are one of the primary causes of spatially correlated changes in Melitaea cinxia population sizes. In Paper (II) spatiotemporal characteristics of Socupulini moths description, diversity and distribution were analysed at a world-wide scale and for the first time GIS techniques were used for Scopulini moth geographical distribution analysis. This study revealed that Scopulini moths have a cosmopolitan distribution. The majority of the species have been described from the low latitudes, sub-Saharan Africa being the hot spot of species diversity. However, the taxonomical effort has been uneven among biogeographical regions. Paper III showed that forest cover change can be analysed in great detail using modern airborne imagery techniques and historical aerial photographs. However, when spatiotemporal forest cover change is studied care has to be taken in co-registration and image interpretation when historical black and white aerial photography is used. In Paper (IV) human population distribution and abundance could be modelled with fairly good results using geospatial predictors and non-Gaussian predictive modelling techniques. Moreover, land cover layer is not necessary needed as a predictor because first and second-order image texture measurements derived from satellite imagery had more power to explain the variation in dwelling unit occurrence and abundance. Paper V showed that generalized linear model (GLM) is a suitable technique for fire occurrence prediction and for burned area estimation. GLM based burned area estimations were found to be more superior than the existing MODIS burned area product (MCD45A1). However, spatial autocorrelation of fires has to be taken into account when using the GLM technique for fire occurrence prediction. Paper VI showed that novel statistical predictive modelling techniques can be used to improve fire prediction, burned area estimation and fire risk mapping at a regional scale. However, some noticeable variation between different predictive modelling techniques for fire occurrence prediction and burned area estimation existed.

Investigating physical properties of solid dosage forms during pharmaceutical processing : Process analytical applications of vibrational spectroscopy

In order to improve and continuously develop the quality of pharmaceutical products, the process analytical technology (PAT) framework has been adopted by the US Food and Drug Administration. One of the aims of PAT is to identify critical process parameters and their effect on the quality of the final product. Real time analysis of the process data enables better control of the processes to obtain a high quality product. The main purpose of this work was to monitor crucial pharmaceutical unit operations (from blending to coating) and to examine the effect of processing on solid-state transformations and physical properties. The tools used were near-infrared (NIR) and Raman spectroscopy combined with multivariate data analysis, as well as X-ray powder diffraction (XRPD) and terahertz pulsed imaging (TPI). To detect process-induced transformations in active pharmaceutical ingredients (APIs), samples were taken after blending, granulation, extrusion, spheronisation, and drying. These samples were monitored by XRPD, Raman, and NIR spectroscopy showing hydrate formation in the case of theophylline and nitrofurantoin. For erythromycin dihydrate formation of the isomorphic dehydrate was critical. Thus, the main focus was on the drying process. NIR spectroscopy was applied in-line during a fluid-bed drying process. Multivariate data analysis (principal component analysis) enabled detection of the dehydrate formation at temperatures above 45°C. Furthermore, a small-scale rotating plate device was tested to provide an insight into film coating. The process was monitored using NIR spectroscopy. A calibration model, using partial least squares regression, was set up and applied to data obtained by in-line NIR measurements of a coating drum process. The predicted coating thickness agreed with the measured coating thickness. For investigating the quality of film coatings TPI was used to create a 3-D image of a coated tablet. With this technique it was possible to determine coating layer thickness, distribution, reproducibility, and uniformity. In addition, it was possible to localise defects of either the coating or the tablet. It can be concluded from this work that the applied techniques increased the understanding of physico-chemical properties of drugs and drug products during and after processing. They additionally provided useful information to improve and verify the quality of pharmaceutical dosage forms

Young Men s Sexual Behaviour in Finland and Estonia : Opportunities for prevention of sexually transmitted infections

Background: The incidence of sexually transmitted infections (STIs) in most EU states has gradually increased and the rate of newly diagnosed HIV cases has doubled since 1999. STIs differ in their clinical features, prognosis and transmission dynamics, though they do share a common factor in their mode of transmission −that is, human behaviour. The evolvement of STI epidemiology involves a joint action of biological, epidemiological and societal factors. Of the more immediate factors, besides timely diagnosis and appropriate treatment, STI incidence is influenced by population patterns of sexual risk behaviour, particularly the number of sexual partners and the frequency of unprotected intercourse. Assessment of sexual behaviour, its sociodemographic determinants and time-trends are important in understanding the distribution and dynamic of STI epidemiology. Additionally, in the light of the basic structural determinants, such as increased level of migration, changes in gender dynamics and impacts from globalization, with its increasing alignment of values and beliefs, can reveal future challenges related to STI epidemiology. STI case surveillance together with surveillance on sexual behaviour can guide the identification of preventive strategies, assess their effectiveness and predict emerging trends. The objective of this study was to provide base line data on sexual risk behaviour, self-reported STIs and their patterns by sociodemographic factors as well as associations of sexual risk behaviour with substance use among young men in Finland and Estonia. In Finland national population based data on adult men s sexual behaviour is limited. The findings are discussed in the context of STI epidemiology as well as their possible implications for public health policies and prevention strategies. Materials and Methods: Data from three different cross-sectional population-based surveys conducted in Finland and Estonia, during 1998 2005, were used. Sexual behaviour- and health-related questions were incorporated in two surveys in Finland; the Health 2000, a large scale general health survey, focussed on young adults, and the Military health behavioural survey on military conscripts participating in the mandatory military training. Through research collaboration with Estonia, similar questions to the Finnish surveys were introduced to the second Estonian HIV/AIDS survey, which was targeted at young adults. All surveys applied mail-returned, anonymous, self-administered questionnaires with multiple choice formatted answers. Results: In Finland, differences in sexual behaviour between young men and women were minor. An age-stratified analysis revealed that the sex-related difference observed in the youngest age group (18 19 years) levelled off in the age group 20 24 and almost disappeared among those aged 25 29. Marital status was the most important sociodemographic correlate for sexual behaviour for both sexes, singles reporting higher numbers of lifetime-partners and condom use. This effect was stronger for women than for men. However, of those who had sex with casual partners, 15% were married or co-habiting, with no difference between male and female respondents. According to the Military health behavioural survey, young men s sexual risk behaviour in Finland did not markedly change over a period of time between 1998 and 2005. Approximately 30−40% of young men had had multiple sex partners (more than five) in their lifetime, over 20% reported having had multiple sex partners (at least three) over the past year and 50% did not use a condom in their last sexual intercourse. Some 10% of men reported accumulation of risk factors, i.e. having had both, multiple sex partners and not used a condom in their last intercourse, over the past year of the survey. When differences and similarities were viewed within Finland and Estonia, a clear sociodemographic patterning of sexual risk behaviour and self-reported STIs was found in Finland, but a somewhat less consistent trend in Estonia. Generally, both, alcohol and drug use were strong correlates for sexual risk behaviour and self-reported STIs in Finland and Estonia, having a greater effect on engagement with multiple sex partners rather than unprotected intercourse or self-reported STIs. In Finland alcohol use, relative to drug use, was a stronger predictor of sexual risk behaviour and self-reported STIs, while in Estonia drug use predicted sexual risk behaviour and self-reported STIs stronger than alcohol use. Conclusions: The study results point to the importance for prevention of sexual risk behaviour, particularly strategies that integrate sexual risk with alcohol and drug use risks. The results point to the need to focus further research on sexual behaviour and STIs among young people; on tracking trends among general population as well as applying in-depth research to identify and learn from vulnerable and high-risk population groups for STIs who are exposed to a combination of risk factors.

Obesity, Smoking and Dieting

Lihavuus ja ylipaino ovat viime vuosikymmeninä yleistyneet; jo yli puolet länsimaiden väestöstä on ylipainoisia ja viidennes lihavia. Varsinkin nuorilla ylipainon lisääntyminen on ollut nopeaa. Ylipaino, erityisesti yhdistettynä vyötärölihavuuteen, sekä tupakointi lisäävät sairastavuutta sydän- ja verisuonisairauksiin, metabolisiin sairauksiin, kuten diabetekseen, sekä moniin syöpiin. Lihavuus ja tupakointi ovatkin kehittyneiden maiden tärkeimpiä ehkäistävissä olevia kuolinsyitä. Samanaikaisesti ylipainon kanssa laihduttaminen ja jopa terveydelle haitalliset laihdutusmenetelmät, kuten tupakointi painonhallintakeinona on tullut yhä yleisemmäksi. Nopeaan painonpudotukseen tähtäävällä laihduttamisella on usein terveydelle haitallisia seurauksia kuten painon nousu yli alkuperäisen painon ja kehon rasvajakauman muuttuminen epäterveellisemmäksi. Kolme neljännestä merkittävästi laihduttaneista kertoo painon nousseen takaisin. Tupakoinnin ja toistuvan laihduttamisen vaikutukset ylipainon ja lihavuuden kehittymiselle kytkeytyvät toisiinsa. Tässä väitöskirjatyössä tutkittiin toistuvan laihduttamisen ja tupakoinnin vaikutusta kehon painoon ja lisäksi tupakoinnin vaikutusta vyötärölihavuuden kehittymiseen. Työn toisena tavoitteena oli tutkia, kuinka voimakkaasti tupakointi ja toistuva laihduttaminen liittyvät toisiinsa suomalaisilla ja onko tämä yhteys erilainen eri ikäryhmissä ja sukupuolilla. Työ perustuu kolmeen laajaan kyselyaineistoon: Nuorten Kaksosten Terveystutkimuksen (englanniksi FinnTwin16) aineistossa on seurattu 1975-79 syntyneitä kaksosia 16, 17, 18 ja 24 vuoden ikäisinä (N=5563). Suomen kaksoskohortin aineisto (N= 12 793) on kerätty vuonna 1990 samaa sukupuolta olevilta, vuosina 1930-57 syntyneiltä kaksosilta. Entisten huippu-urheilijoiden (N=1838) ja heille kaltaistettujen verrokkien (N=834) seurantatutkimuksessa tiedot on kerätty vuosina 1985, 1995 ja 2001. Pituus, paino ja tupakointi on kysytty kaikissa kyselyissä. Kaksoset vastasivat laihdutuskäyttäytymistä koskeviin kysymyksiin. Urheilijoiden laihdutuskäyttäytyminen pääteltiin lajin perusteella, sillä toistuvan laihduttamisen tiedetään olevan yleistä painoluokissa urheilevilla urheilijoilla (esim.painijat, nyrkkeilijät). Nuoruusiän tupakointi ennusti vyötärölihavuutta molemmilla sukupuolilla ja lisäksi ylipainoisuutta naisilla. Toistuva laihduttaminen oli yhteydessä myöhempään painonnousuun ja lihavuuteen miehillä. Lisäksi toistuvan laihduttamisen ja tupakoinnin todettiin liittyvän toisiinsa nuorilla aikuisilla. Vanhemmissa ikäluokissa miehet, jotka tupakoivat, laihduttivat harvemmin kuin tupakoimattomat. Lihavuuteen ja vyötärölihavuuteen liittyvän oheissairastavuuden ennaltaehkäisyssä tupakoinnin ja toistuvan laihduttamisen vähentäminen saattavat olla aiemmin luultua tehokkaampia keinoja.

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

What kind of data would ascertain real periodicity in the terrestrial impact crater record

Maan törmäyskraaterien ikäjakauman mahdollinen ajallinen jaksollisuus on herättänyt laajaa keskustelua sen jälkeen, kun ilmiö ensimmäistä kertaa raportoitiin joukossa arvostettuja tieteellisiä artikkeleita vuonna 1984. Vaikka nykytiedon valossa on kyseenalaista perustuuko havaittu jaksollisuus todelliseen fysikaaliseen ilmiöön, on kuitenkin mahdollista, että jaksollisuus on todella olemassa ja se voitaisiin havaita laajemmalla ja tarkemmalla törmäyskraateriaineistolla. Tutkimuksessa luotiin simuloidut kraaterien ajalliset tiheys- ja kertymäfunktiot tapauksille, jossa kraaterit syntyvät joko täysin jaksollisella tai satunnaisella prosessilla. Näiden kahden ääritapauksen lisäksi luotiin jakaumat myös kahdelle niiden yhdistelmälle. Nämä mallit mahdollistavat myös erilaisten kraaterien iänmäärityksen epätarkkuuksien huomioonottamisen. Näistä jakaumista luotiin eri pituisia simuloituja kraaterien ikien aikasarjoja. Lopulta simuloiduista aikasarjoista pyrittiin Rayleigh'n menetelmän avulla etsimään jakaumassa ollutta jaksollisuutta. Tutkimuksemme perusteella ajallisen jaksollisuuden havaitseminen kraateriaikasarjoista on lähes mahdotonta mikäli vain yksi kolmasosa kraatereista on jaksollisen ilmiön aiheuttamia, vaikka nykyistä kraateriaineistoa laajempi ja tarkempi aineisto olisi tulevaisuudessa saatavilla. Mikäli kaksi kolmasosaa meteoriittitörmäyksistä on jaksollisia, sen havaitseminen on mahdollista, mutta vaatii huomattavasti tämän hetkistä kattavamman kraateriaineiston. Tutkimuksen perusteella on syytä epäillä, että havaittu kraaterien ajallinen jaksollisuus ei ole todellinen ilmiö.

On probability-based inference under data missing by design

Whether a statistician wants to complement a probability model for observed data with a prior distribution and carry out fully probabilistic inference, or base the inference only on the likelihood function, may be a fundamental question in theory, but in practice it may well be of less importance if the likelihood contains much more information than the prior. Maximum likelihood inference can be justified as a Gaussian approximation at the posterior mode, using flat priors. However, in situations where parametric assumptions in standard statistical models would be too rigid, more flexible model formulation, combined with fully probabilistic inference, can be achieved using hierarchical Bayesian parametrization. This work includes five articles, all of which apply probability modeling under various problems involving incomplete observation. Three of the papers apply maximum likelihood estimation and two of them hierarchical Bayesian modeling. Because maximum likelihood may be presented as a special case of Bayesian inference, but not the other way round, in the introductory part of this work we present a framework for probability-based inference using only Bayesian concepts. We also re-derive some results presented in the original articles using the toolbox equipped herein, to show that they are also justifiable under this more general framework. Here the assumption of exchangeability and de Finetti's representation theorem are applied repeatedly for justifying the use of standard parametric probability models with conditionally independent likelihood contributions. It is argued that this same reasoning can be applied also under sampling from a finite population. The main emphasis here is in probability-based inference under incomplete observation due to study design. This is illustrated using a generic two-phase cohort sampling design as an example. The alternative approaches presented for analysis of such a design are full likelihood, which utilizes all observed information, and conditional likelihood, which is restricted to a completely observed set, conditioning on the rule that generated that set. Conditional likelihood inference is also applied for a joint analysis of prevalence and incidence data, a situation subject to both left censoring and left truncation. Other topics covered are model uncertainty and causal inference using posterior predictive distributions. We formulate a non-parametric monotonic regression model for one or more covariates and a Bayesian estimation procedure, and apply the model in the context of optimal sequential treatment regimes, demonstrating that inference based on posterior predictive distributions is feasible also in this case.

Modeling Asymmetries in Financial Data with Multiplicative Error Models

This thesis addresses modeling of financial time series, especially stock market returns and daily price ranges. Modeling data of this kind can be approached with so-called multiplicative error models (MEM). These models nest several well known time series models such as GARCH, ACD and CARR models. They are able to capture many well established features of financial time series including volatility clustering and leptokurtosis. In contrast to these phenomena, different kinds of asymmetries have received relatively little attention in the existing literature. In this thesis asymmetries arise from various sources. They are observed in both conditional and unconditional distributions, for variables with non-negative values and for variables that have values on the real line. In the multivariate context asymmetries can be observed in the marginal distributions as well as in the relationships of the variables modeled. New methods for all these cases are proposed. Chapter 2 considers GARCH models and modeling of returns of two stock market indices. The chapter introduces the so-called generalized hyperbolic (GH) GARCH model to account for asymmetries in both conditional and unconditional distribution. In particular, two special cases of the GARCH-GH model which describe the data most accurately are proposed. They are found to improve the fit of the model when compared to symmetric GARCH models. The advantages of accounting for asymmetries are also observed through Value-at-Risk applications. Both theoretical and empirical contributions are provided in Chapter 3 of the thesis. In this chapter the so-called mixture conditional autoregressive range (MCARR) model is introduced, examined and applied to daily price ranges of the Hang Seng Index. The conditions for the strict and weak stationarity of the model as well as an expression for the autocorrelation function are obtained by writing the MCARR model as a first order autoregressive process with random coefficients. The chapter also introduces inverse gamma (IG) distribution to CARR models. The advantages of CARR-IG and MCARR-IG specifications over conventional CARR models are found in the empirical application both in- and out-of-sample. Chapter 4 discusses the simultaneous modeling of absolute returns and daily price ranges. In this part of the thesis a vector multiplicative error model (VMEM) with asymmetric Gumbel copula is found to provide substantial benefits over the existing VMEM models based on elliptical copulas. The proposed specification is able to capture the highly asymmetric dependence of the modeled variables thereby improving the performance of the model considerably. The economic significance of the results obtained is established when the information content of the volatility forecasts derived is examined.

Computationally Efficient Methods for MDL-Optimal Density Estimation and Data Clustering

The Minimum Description Length (MDL) principle is a general, well-founded theoretical formalization of statistical modeling. The most important notion of MDL is the stochastic complexity, which can be interpreted as the shortest description length of a given sample of data relative to a model class. The exact definition of the stochastic complexity has gone through several evolutionary steps. The latest instantation is based on the so-called Normalized Maximum Likelihood (NML) distribution which has been shown to possess several important theoretical properties. However, the applications of this modern version of the MDL have been quite rare because of computational complexity problems, i.e., for discrete data, the definition of NML involves an exponential sum, and in the case of continuous data, a multi-dimensional integral usually infeasible to evaluate or even approximate accurately. In this doctoral dissertation, we present mathematical techniques for computing NML efficiently for some model families involving discrete data. We also show how these techniques can be used to apply MDL in two practical applications: histogram density estimation and clustering of multi-dimensional data.

Where and how to conserve: Extending the scope of spatial reserve network design

Ongoing habitat loss and fragmentation threaten much of the biodiversity that we know today. As such, conservation efforts are required if we want to protect biodiversity. Conservation budgets are typically tight, making the cost-effective selection of protected areas difficult. Therefore, reserve design methods have been developed to identify sets of sites, that together represent the species of conservation interest in a cost-effective manner. To be able to select reserve networks, data on species distributions is needed. Such data is often incomplete, but species habitat distribution models (SHDMs) can be used to link the occurrence of the species at the surveyed sites to the environmental conditions at these locations (e.g. climatic, vegetation and soil conditions). The probability of the species occurring at unvisited location is next predicted by the model, based on the environmental conditions of those sites. The spatial configuration of reserve networks is important, because habitat loss around reserves can influence the persistence of species inside the network. Since species differ in their requirements for network configuration, the spatial cohesion of networks needs to be species-specific. A way to account for species-specific requirements is to use spatial variables in SHDMs. Spatial SHDMs allow the evaluation of the effect of reserve network configuration on the probability of occurrence of the species inside the network. Even though reserves are important for conservation, they are not the only option available to conservation planners. To enhance or maintain habitat quality, restoration or maintenance measures are sometimes required. As a result, the number of conservation options per site increases. Currently available reserve selection tools do however not offer the ability to handle multiple, alternative options per site. This thesis extends the existing methodology for reserve design, by offering methods to identify cost-effective conservation planning solutions when multiple, alternative conservation options are available per site. Although restoration and maintenance measures are beneficial to certain species, they can be harmful to other species with different requirements. This introduces trade-offs between species when identifying which conservation action is best applied to which site. The thesis describes how the strength of such trade-offs can be identified, which is useful for assessing consequences of conservation decisions regarding species priorities and budget. Furthermore, the results of the thesis indicate that spatial SHDMs can be successfully used to account for species-specific requirements for spatial cohesion - in the reserve selection (single-option) context as well as in the multi-option context. Accounting for the spatial requirements of multiple species and allowing for several conservation options is however complicated, due to trade-offs in species requirements. It is also shown that spatial SHDMs can be successfully used for gaining information on factors that drive a species spatial distribution. Such information is valuable to conservation planning, as better knowledge on species requirements facilitates the design of networks for species persistence. This methods and results described in this thesis aim to improve species probabilities of persistence, by taking better account of species habitat and spatial requirements. Many real-world conservation planning problems are characterised by a variety of conservation options related to protection, restoration and maintenance of habitat. Planning tools therefore need to be able to incorporate multiple conservation options per site, in order to continue the search for cost-effective conservation planning solutions. Simultaneously, the spatial requirements of species need to be considered. The methods described in this thesis offer a starting point for combining these two relevant aspects of conservation planning.

The range expansion of the European map butterfly in Finland

Climate change will influence the living conditions of all life on Earth. For some species the change in the environmental conditions that has occurred so far has already increased the risk of extinction, and the extinction risk is predicted to increase for large numbers of species in the future. Some species may have time to adapt to the changing environmental conditions, but the rate and magnitude of the change are too great to allow many species to survive via evolutionary changes. Species responses to climate change have been documented for some decades. Some groups of species, like many insects, respond readily to changes in temperature conditions and have shifted their distributions northwards to new climatically suitable regions. Such range shifts have been well documented especially in temperate zones. In this context, butterflies have been studied more than any other group of species, partly for the reason that their past geographical ranges are well documented, which facilitates species-climate modelling and other analyses. The aim of the modelling studies is to examine to what extent shifts in species distributions can be explained by climatic and other factors. Models can also be used to predict the future distributions of species. In this thesis, I have studied the response to climate change of one species of butterfly within one geographically restricted area. The study species, the European map butterfly (Araschnia levana), has expanded rapidly northwards in Finland during the last two decades. I used statistical and dynamic modelling approaches in combination with field studies to analyse the effects of climate warming and landscape structure on the expansion. I studied possible role of molecular variation in phosphoglucose isomerase (PGI), a glycolytic enzyme affecting flight metabolism and thereby flight performance, in the observed expansion of the map butterfly at two separate expansion fronts in Finland. The expansion rate of the map butterfly was shown to be correlated with the frequency of warmer than average summers during the study period. The result is in line with the greater probability of occurrence of the second generation during warm summers and previous results on this species showing greater mobility of the second than first generation individuals. The results of a field study in this thesis indicated low mobility of the first generation butterflies. Climatic variables alone were not sufficient to explain the observed expansion in Finland. There are also problems in transferring the climate model to new regions from the ones from which data were available to construct the model. The climate model predicted a wider distribution in the south-western part of Finland than what has been observed. Dynamic modelling of the expansion in response to landscape structure suggested that habitat and landscape structure influence the rate of expansion. In southern Finland the landscape structure may have slowed down the expansion rate. The results on PGI suggested that allelic variation in this enzyme may influence flight performance and thereby the rate of expansion. Genetic differences of the populations at the two expansion fronts may explain at least partly the observed differences in the rate of expansion. Individuals with the genotype associated with high flight metabolic rate were most frequent in eastern Finland, where the rate of range expansion has been highest.

Phospholipids of lipid-containing bacteriophages and their transbilayer distribution

In this study we used electro-spray ionization mass-spectrometry to determine phospholipid class and molecular species compositions in bacteriophages PM2, PRD1, Bam35 and phi6 as well as their hosts. To obtain compositional data of the individual leaflets, phospholipid transbilayer distribution in the viral membranes was studied. We found that 1) the membranes of all studied bacteriophage are enriched in PG as compared to the host membranes, 2) molecular species compositions in the phage and host membranes are similar, and 3) phospholipids in the viral membranes are distributed asymmetrically with phosphatidylglycerol enriched in the outer leaflet and phosphatidylethanolamine in the inner one (except Bam35). Alternative models for selective incorporation of phospholipids to phages and for the origins of the asymmetric phospholipid transbilayer distribution are discussed. Notably, the present data are also useful when constructing high resolution structural models of bacteriophages, since diffraction methods cannot provide a detailed structure of the membrane due to high motility of the lipids and lack of symmetric organization of membrane proteins.

Distribution of non-biting midges (Diptera, Chironomidae) in subarctic lakes of Finnish Lapland applications in lake classification and palaeolimnology

Climate change contributes directly or indirectly to changes in species distributions, and there is very high confidence that recent climate warming is already affecting ecosystems. The Arctic has already experienced the greatest regional warming in recent decades, and the trend is continuing. However, studies on the northern ecosystems are scarce compared to more southerly regions. Better understanding of the past and present environmental change is needed to be able to forecast the future. Multivariate methods were used to explore the distributional patterns of chironomids in 50 shallow (≤ 10m) lakes in relation to 24 variables determined in northern Fennoscandia at the ecotonal area from the boreal forest in the south to the orohemiarctic zone in the north. Highest taxon richness was noted at middle elevations around 400 m a.s.l. Significantly lower values were observed from cold lakes situated in the tundra zone. Lake water alkalinity had the strongest positive correlation with the taxon richness. Many taxa had preference for lakes either on tundra area or forested area. The variation in the chironomid abundance data was best correlated with sediment organic content (LOI), lake water total organic carbon content, pH and air temperature, with LOI being the strongest variable. Three major lake groups were separated on the basis of their chironomid assemblages: (i) small and shallow organic-rich lakes, (ii) large and base-rich lakes, and (iii) cold and clear oligotrophic tundra lakes. Environmental variables best discriminating the lake groups were LOI, taxon richness, and Mg. When repeated, this kind of an approach could be useful and efficient in monitoring the effects of global change on species ranges. Many species of fast spreading insects, including chironomids, show a remarkable ability to track environmental changes. Based on this ability, past environmental conditions have been reconstructed using their chitinous remains in the lake sediment profiles. In order to study the Holocene environmental history of subarctic aquatic systems, and quantitatively reconstruct the past temperatures at or near the treeline, long sediment cores covering the last 10000 years (the Holocene) were collected from three lakes. Lower temperature values than expected based on the presence of pine in the catchment during the mid-Holocene were reconstructed from a lake with great water volume and depth. The lake provided thermal refuge for profundal, cold adapted taxa during the warm period. In a shallow lake, the decrease in the reconstructed temperatures during the late Holocene may reflect the indirect response of the midges to climate change through, e.g., pH change. The results from three lakes indicated that the response of chironomids to climate have been more or less indirect. However, concurrent shifts in assemblages of chironomids and vegetation in two lakes during the Holocene time period indicated that the midges together with the terrestrial vegetation had responded to the same ultimate cause, which most likely was the Holocene climate change. This was also supported by the similarity in the long-term trends in faunal succession for the chironomid assemblages in several lakes in the area. In northern Finnish Lapland the distribution of chironomids were significantly correlated with physical and limnological factors that are most likely to change as a result of future climate change. The indirect and individualistic response of aquatic systems, as reconstructed using the chironomid assemblages, to the climate change in the past suggests that in the future, the lake ecosystems in the north do not respond in one predictable way to the global climate change. Lakes in the north may respond to global climate change in various ways that are dependent on the initial characters of the catchment area and the lake.