Modeling ecological and evolutionary processes in spatially structured populations

Many species inhabit fragmented landscapes, resulting either from anthropogenic or from natural processes. The ecological and evolutionary dynamics of spatially structured populations are affected by a complex interplay between endogenous and exogenous factors. The metapopulation approach, simplifying the landscape to a discrete set of patches of breeding habitat surrounded by unsuitable matrix, has become a widely applied paradigm for the study of species inhabiting highly fragmented landscapes. In this thesis, I focus on the construction of biologically realistic models and their parameterization with empirical data, with the general objective of understanding how the interactions between individuals and their spatially structured environment affect ecological and evolutionary processes in fragmented landscapes. I study two hierarchically structured model systems, which are the Glanville fritillary butterfly in the Åland Islands, and a system of two interacting aphid species in the Tvärminne archipelago, both being located in South-Western Finland. The interesting and challenging feature of both study systems is that the population dynamics occur over multiple spatial scales that are linked by various processes. My main emphasis is in the development of mathematical and statistical methodologies. For the Glanville fritillary case study, I first build a Bayesian framework for the estimation of death rates and capture probabilities from mark-recapture data, with the novelty of accounting for variation among individuals in capture probabilities and survival. I then characterize the dispersal phase of the butterflies by deriving a mathematical approximation of a diffusion-based movement model applied to a network of patches. I use the movement model as a building block to construct an individual-based evolutionary model for the Glanville fritillary butterfly metapopulation. I parameterize the evolutionary model using a pattern-oriented approach, and use it to study how the landscape structure affects the evolution of dispersal. For the aphid case study, I develop a Bayesian model of hierarchical multi-scale metapopulation dynamics, where the observed extinction and colonization rates are decomposed into intrinsic rates operating specifically at each spatial scale. In summary, I show how analytical approaches, hierarchical Bayesian methods and individual-based simulations can be used individually or in combination to tackle complex problems from many different viewpoints. In particular, hierarchical Bayesian methods provide a useful tool for decomposing ecological complexity into more tractable components.

Environmental factors and uncertainty in fisheries management in the northern Baltic Sea

The Baltic Sea is a geologically young, large brackish water basin, and few of the species living there have fully adapted to its special conditions. Many of the species live on the edge of their distribution range in terms of one or more environmental variables such as salinity or temperature. Environmental fluctuations are know to cause fluctuations in populations abundance, and this effect is especially strong near the edges of the distribution range, where even small changes in an environmental variable can be critical to the success of a species. This thesis examines which environmental factors are the most important in relation to the success of various commercially exploited fish species in the northern Baltic Sea. It also examines the uncertainties related to fish stocks current and potential status as well as to their relationship with their environment. The aim is to quantify the uncertainties related to fisheries and environmental management, to find potential management strategies that can be used to reduce uncertainty in management results and to develop methodology related to uncertainty estimation in natural resources management. Bayesian statistical methods are utilized due to their ability to treat uncertainty explicitly in all parts of the statistical model. The results show that uncertainty about important parameters of even the most intensively studied fish species such as salmon (Salmo salar L.) and Baltic herring (Clupea harengus membras L.) is large. On the other hand, management approaches that reduce uncertainty can be found. These include utilising information about ecological similarity of fish stocks and species, and using management variables that are directly related to stock parameters that can be measured easily and without extrapolations or assumptions.

Statistical tools for analyzing water quality data

Water quality data are often collected at different sites over time to improve water quality management. Water quality data usually exhibit the following characteristics: non-normal distribution, presence of outliers, missing values, values below detection limits (censored), and serial dependence. It is essential to apply appropriate statistical methodology when analyzing water quality data to draw valid conclusions and hence provide useful advice in water management. In this chapter, we will provide and demonstrate various statistical tools for analyzing such water quality data, and will also introduce how to use a statistical software R to analyze water quality data by various statistical methods. A dataset collected from the Susquehanna River Basin will be used to demonstrate various statistical methods provided in this chapter. The dataset can be downloaded from website http://www.srbc.net/programs/CBP/nutrientprogram.htm.

Site characterization model using least-square support vector machine and relevance vector machine based on corrected SPT data (N-c)

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N,), N values have been corrected (Ne) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three-dimensional site characterization model, the function N-c=N-c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N, value, is to be approximated in which N, value at any half-space point in Bangalore can be determined. The first algorithm uses least-square support vector machine (LSSVM), which is related to aridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel-based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright (C) 2009 John Wiley & Sons,Ltd.

Agricultural expansion and climate change in the Taita Hills, Kenya: an assessment of potential environmental impacts

The indigenous cloud forests in the Taita Hills have suffered substantial degradation for several centuries due to agricultural expansion. Currently, only 1% of the original forested area remains preserved in this region. Furthermore, climate change imposes an imminent threat for local economy and environmental sustainability. In such circumstances, elaborating tools to conciliate socioeconomic growth and natural resources conservation is an enormous challenge. This dissertation tackles essential aspects for understanding the ongoing agricultural activities in the Taita Hills and their potential environmental consequences in the future. Initially, alternative methods were designed to improve our understanding of the ongoing agricultural activities. Namely, methods for agricultural survey planning and to estimate evapotranspiration were evaluated, taking into account a number of limitations regarding data and resources availability. Next, this dissertation evaluates how upcoming agricultural expansion, together with climate change, will affect the natural resources in the Taita Hills up to the year 2030. The driving forces of agricultural expansion in the region were identified as aiming to delineate future landscape scenarios and evaluate potential impacts from the soil and water conservation point of view. In order to investigate these issues and answer the research questions, this dissertation combined state of the art modelling tools with renowned statistical methods. The results indicate that, if current trends persist, agricultural areas will occupy roughly 60% of the study area by 2030. Although the simulated land use changes will certainly increase soil erosion figures, new croplands are likely to come up predominantly in the lowlands, which comprise areas with lower soil erosion potential. By 2030, rainfall erosivity is likely to increase during April and November due to climate change. Finally, this thesis addressed the potential impacts of agricultural expansion and climate changes on Irrigation Water Requirements (IWR), which is considered another major issue in the context of the relations between land use and climate. Although the simulations indicate that climate change will likely increase annual volumes of rainfall during the following decades, IWR will continue to increase due to agricultural expansion. By 2030, new cropland areas may cause an increase of approximately 40% in the annual volume of water necessary for irrigation.

Why Don't Students Talk? Causes of Foreign Language Communication Apprehension in English Classes: A Comparison of High Apprehensive and Low Apprehensive Upper Secondary Students.

OBJECTIVES. Oral foreign language skills are an integral part of one's social, academic and professional competence. This can be problematic for those suffering from foreign language communication apprehension (CA), or a fear of speaking a foreign language. CA manifests itself, for example, through feelings of anxiety and tension, physical arousal and avoidance of foreign language communication situations. According to scholars, foreign language CA may impede the language learning process significantly and have detrimental effects on one's language learning, academic achievement and career prospects. Drawing on upper secondary students' subjective experiences of communication situations in English as a foreign language, this study seeks, first, to describe, analyze and interpret why upper secondary students experience English language communication apprehension in English as a foreign language (EFL) classes. Second, this study seeks to analyse what the most anxiety-arousing oral production tasks in EFL classes are, and which features of different oral production tasks arouse English language communication apprehension and why. The ultimate objectives of the present study are to raise teachers' awareness of foreign language CA and its features, manifestations and impacts in foreign language classes as well as to suggest possible ways to minimize the anxiety-arousing features in foreign language classes. METHODS. The data was collected in two phases by means of six-part Likert-type questionnaires and theme interviews, and analysed using both quantitative and qualitative methods. The questionnaire data was collected in spring 2008. The respondents were 122 first-year upper secondary students, 68 % of whom were girls and 31 % of whom were boys. The data was analysed by statistical methods using SPSS software. The theme interviews were conducted in spring 2009. The interviewees were 11 second-year upper secondary students aged 17 to 19, who were chosen by purposeful selection on the basis of their English language CA level measured in the questionnaires. Six interviewees were classified as high apprehensives and five as low apprehensives according to their score in the foreign language CA scale in the questionnaires. The interview data was coded and thematized using the technique of content analysis. The analysis and interpretation of the data drew on a comparison of the self-reports of the highly apprehensive and low apprehensive upper secondary students. RESULTS. The causes of English language CA in EFL classes as reported by the students were both internal and external in nature. The most notable causes were a low self-assessed English proficiency, a concern over errors, a concern over evaluation, and a concern over the impression made on others. Other causes related to a high English language CA were a lack of authentic oral practise in EFL classes, discouraging teachers and negative experiences of learning English, unrealistic internal demands for oral English performance, high external demands and expectations for oral English performance, the conversation partner's higher English proficiency, and the audience's large size and unfamiliarity. The most anxiety-arousing oral production tasks in EFL classes were presentations or speeches with or without notes in front of the class, acting in front of the class, pair debates with the class as audience, expressing thoughts and ideas to the class, presentations or speeches without notes while seated, group debates with the class as audience, and answering to the teacher's questions involuntarily. The main features affecting the anxiety-arousing potential of an oral production task were a high degree of attention, a large audience, a high degree of evaluation, little time for preparation, little linguistic support, and a long duration.

Language universals and linguistic complexity : Three case studies in core argument marking

In this dissertation I study language complexity from a typological perspective. Since the structuralist era, it has been assumed that local complexity differences in languages are balanced out in cross-linguistic comparisons and that complexity is not affected by the geopolitical or sociocultural aspects of the speech community. However, these assumptions have seldom been studied systematically from a typological point of view. My objective is to define complexity so that it is possible to compare it across languages and to approach its variation with the methods of quantitative typology. My main empirical research questions are: i) does language complexity vary in any systematic way in local domains, and ii) can language complexity be affected by the geographical or social environment? These questions are studied in three articles, whose findings are summarized in the introduction to the dissertation. In order to enable cross-language comparison, I measure complexity as the description length of the regularities in an entity; I separate it from difficulty, focus on local instead of global complexity, and break it up into different types. This approach helps avoid the problems that plagued earlier metrics of language complexity. My approach to grammar is functional-typological in nature, and the theoretical framework is basic linguistic theory. I delimit the empirical research functionally to the marking of core arguments (the basic participants in the sentence). I assess the distributions of complexity in this domain with multifactorial statistical methods and use different sampling strategies, implementing, for instance, the Greenbergian view of universals as diachronic laws of type preference. My data come from large and balanced samples (up to approximately 850 languages), drawn mainly from reference grammars. The results suggest that various significant trends occur in the marking of core arguments in regard to complexity and that complexity in this domain correlates with population size. These results provide evidence that linguistic patterns interact among themselves in terms of complexity, that language structure adapts to the social environment, and that there may be cognitive mechanisms that limit complexity locally. My approach to complexity and language universals can therefore be successfully applied to empirical data and may serve as a model for further research in these areas.

The Commercial Structure of Pedestrian Streets and Shopping Districts - Three Cases from Finland

Kävelykadut ovat tunnustettu tapa elävöittää keskusta-alueiden kauppaa. Aluksi moni kauppias epäilee kävelykadun tuomia muutoksia, mutta kokemus osoittaa, että kävelykadut ovat olleet menestyksekkäitä ja nostavat siellä olevien yritysten myyntiä. Jotkut yritykset eivät kuitenkin hyödy kävelykaduista, kun taas toiset hyötyvät paljon kun katu muuttuu kävelykaduksi. Tämä pro gradu -tutkielma tutkii kävelykatujen kaupallista rakennetta, jotta saataisiin selville minkätyyppiset yritykset löytyvät kävelykadulta. Tuloksia verrataan sen kaupallisen keskusvyöhykkeen kaupalliseen rakenteeseen missä kävelykatu sijaitsee. Näin saadaan selville erot kaupallisessa rakenteessa. Pro gradu tutkii myös miten tavallisia ketjuyritykset ovat kävelykaduilla ja kaupallisissa keskusvyöhykkeissä. Tutkimusaineisto koottiin kaupallisen inventoinnin avulla, joka suoritettiin kolmessa suomalaisessa kaupungissa: Tammisaaressa, Keravalla ja Porissa. Saatu aineisto luokiteltiin ja tulokset piirrettiin kartalle. Perustilastollisia menetelmiä käytettiin tulosten analysoimisessa. Tulokset eriteltiin kävelykadun, kauppakeskusten ja muiden paikkojen osalta ja luokiteltiin yleisluokkiin vähittäiskauppa, ravintola ja muu palvelu. Tulokset näyttävät, että on olemassa selkeitä eroja kun vertaa kävelykatuja ja kaupallisia keskusvyöhykkeitä. Kävelykaduilla on paljon enemmän vähittäiskauppoja, etenkin muotikauppoja, kuin muilla kaduilla. Kauppakeskuksilla on samantapainen kaupallinen rakenne kuin kävelykaduilla kun taas muilla kaduilla esiintyy vähemmän vähittäiskauppoja ja enemmän palveluyrityksiä. Ravintolat ovat melkein yhtä tavallisia koko kaupallisessa keskusvyöhykkeessä. Ketjuyritysten osalta tulokset ovat epäselviä. On olemassa osviittaa siitä, että ne ovat tavallisempia kävelykaduilla, etenkin suurissa kaupungeissa. Saatua tulosta ei ole kuitenkin tarpeeksi, jotta varmaa tietoa olisi saatu. Viimeisten 10–15 vuoden ajan Suomen kävelykadut ovat muuttuneet enemmän ravintolavaltaisiksi muiden palveluiden kustannuksella. Vähittäiskauppojen määrä on pysynyt vakaana. Suomalaiset kävelykadut eroavat kaupalliselta rakenteeltaan pohjoismaisista kävelykaduista, joilla on enemmän vähittäiskauppoja ja vähemmän palveluyrityksiä. Tapauskohtaisissa tuloksissa esiintyy paljon eroavaisuuksia. Paikalliset tekijät ovat usein voimakkaampia kuin yleiset teoriat kauppojen sijainnista kävelykaduilla. Yleisesti ottaen tulokset tukevat teoreettista viitekehystä. Tulokset antavat tarkempaa tietoa kävelykatujen ja kaupallisten keskusvyöhykkeiden kaupallisesta rakenteesta ja siitä, mitkä tekijät tähän vaikuttaa.

A study of the synthetic methods and properties of graphenes

Graphenes with varying number of layers can be synthesized by using different strategies. Thus, single-layer graphene is prepared by micromechanical cleavage, reduction of single-layer graphene oxide, chemical vapor deposition and other methods. Few-layer graphenes are synthesized by conversion of nanodiamond, arc discharge of graphite and other methods. In this article, we briefly overview the various synthetic methods and the surface, magnetic and electrical properties of the produced graphenes. Few-layer graphenes exhibit ferromagnetic features along with antiferromagnetic properties, independent of the method of preparation. Aside from the data on electrical conductivity of graphenes and graphene-polymer composites, we also present the field-effect transistor characteristics of graphenes. Only single-layer reduced graphene oxide exhibits ambipolar properties. The interaction of electron donor and acceptor molecules with few-layer graphene samples is examined in detail.

Parallel computing concepts and methods for floquet analysis of helicopter trim and stability

Floquet analysis is widely used for small-order systems (say, order M < 100) to find trim results of control inputs and periodic responses, and stability results of damping levels and frequencies, Presently, however, it is practical neither for design applications nor for comprehensive analysis models that lead to large systems (M > 100); the run time on a sequential computer is simply prohibitive, Accordingly, a massively parallel Floquet analysis is developed with emphasis on large systems, and it is implemented on two SIMD or single-instruction, multiple-data computers with 4096 and 8192 processors, The focus of this development is a parallel shooting method with damped Newton iteration to generate trim results; the Floquet transition matrix (FTM) comes out as a byproduct, The eigenvalues and eigenvectors of the FTM are computed by a parallel QR method, and thereby stability results are generated, For illustration, flap and flap-lag stability of isolated rotors are treated by the parallel analysis and by a corresponding sequential analysis with the conventional shooting and QR methods; linear quasisteady airfoil aerodynamics and a finite-state three-dimensional wake model are used, Computational reliability is quantified by the condition numbers of the Jacobian matrices in Newton iteration, the condition numbers of the eigenvalues and the residual errors of the eigenpairs, and reliability figures are comparable in both the parallel and sequential analyses, Compared to the sequential analysis, the parallel analysis reduces the run time of large systems dramatically, and the reduction increases with increasing system order; this finding offers considerable promise for design and comprehensive-analysis applications.

Computational Biology and Language

Current scientific research is characterized by increasing specialization, accumulating knowledge at a high speed due to parallel advances in a multitude of sub-disciplines. Recent estimates suggest that human knowledge doubles every two to three years – and with the advances in information and communication technologies, this wide body of scientific knowledge is available to anyone, anywhere, anytime. This may also be referred to as ambient intelligence – an environment characterized by plentiful and available knowledge. The bottleneck in utilizing this knowledge for specific applications is not accessing but assimilating the information and transforming it to suit the needs for a specific application. The increasingly specialized areas of scientific research often have the common goal of converting data into insight allowing the identification of solutions to scientific problems. Due to this common goal, there are strong parallels between different areas of applications that can be exploited and used to cross-fertilize different disciplines. For example, the same fundamental statistical methods are used extensively in speech and language processing, in materials science applications, in visual processing and in biomedicine. Each sub-discipline has found its own specialized methodologies making these statistical methods successful to the given application. The unification of specialized areas is possible because many different problems can share strong analogies, making the theories developed for one problem applicable to other areas of research. It is the goal of this paper to demonstrate the utility of merging two disparate areas of applications to advance scientific research. The merging process requires cross-disciplinary collaboration to allow maximal exploitation of advances in one sub-discipline for that of another. We will demonstrate this general concept with the specific example of merging language technologies and computational biology.

Examining the Effectiveness of Discriminant Function Analysis and Cluster Analysis in Species Identification of Male Field Crickets Based on Their Calling Songs

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification.

Development and Evaluation of Statistical Downscaling Models for Monthly Precipitation

Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) GCM simulations. The principal component analysis (PCA) and fuzzy c-means clustering method (FCM) are used as part of downscaling model to reduce the dimensionality of the dataset and identify the clusters in the data, respectively. Evaluation of the performances of the models using different error and statistical measures indicates that the SVM-based model performed better than all the other models in reproducing most monthly rainfall statistics at 18 sites. Output from the third-generation CGCM3 GCM for the A1B scenario was used for future projections. For the projection period 2001-10, MLR was used to relate variables at the GCM and NCEP grid scales. Use of MLR in linking the predictor variables at the GCM and NCEP grid scales yielded better reproduction of monthly rainfall statistics at most of the stations (12 out of 18) compared to those by spatial interpolation technique used in earlier studies.

Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8-50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mgha(-1)) at spatial scales ranging from 5 to 250m (0.025-6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20-400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial ``dilution'' bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.

Quorum Sensing and Biofilm Formation in Mycobacteria: Role of c-di-GMP and Methods to Study This Second Messenger

Bacteria have evolved to survive the ever-changing environment using intriguing mechanisms of quorum sensing (QS). Very often, QS facilitates formation of biofilm to help bacteria to persist longer and the formation of such biofilms is regulated by c-di-GMP. It is a well-known second messenger also found in mycobacteria. Several methods have been developed to study c-di-GMP signaling pathways in a variety of bacteria. In this review, we have attempted to highlight a connection between c-di-GMP and biofilm formation and QS in mycobacteria and several methods that have helped in better understanding of c-di-GMP signaling. (c) 2014 IUBMB Life, 66(12):823-834, 2014