Conceptual and statistical modelling of environmental effects in population dynamics

Population dynamics are generally viewed as the result of intrinsic (purely density dependent) and extrinsic (environmental) processes. Both components, and potential interactions between those two, have to be modelled in order to understand and predict dynamics of natural populations; a topic that is of great importance in population management and conservation. This thesis focuses on modelling environmental effects in population dynamics and how effects of potentially relevant environmental variables can be statistically identified and quantified from time series data. Chapter I presents some useful models of multiplicative environmental effects for unstructured density dependent populations. The presented models can be written as standard multiple regression models that are easy to fit to data. Chapters II IV constitute empirical studies that statistically model environmental effects on population dynamics of several migratory bird species with different life history characteristics and migration strategies. In Chapter II, spruce cone crops are found to have a strong positive effect on the population growth of the great spotted woodpecker (Dendrocopos major), while cone crops of pine another important food resource for the species do not effectively explain population growth. The study compares rate- and ratio-dependent effects of cone availability, using state-space models that distinguish between process and observation error in the time series data. Chapter III shows how drought, in combination with settling behaviour during migration, produces asymmetric spatially synchronous patterns of population dynamics in North American ducks (genus Anas). Chapter IV investigates the dynamics of a Finnish population of skylark (Alauda arvensis), and point out effects of rainfall and habitat quality on population growth. Because the skylark time series and some of the environmental variables included show strong positive autocorrelation, the statistical significances are calculated using a Monte Carlo method, where random autocorrelated time series are generated. Chapter V is a simulation-based study, showing that ignoring observation error in analyses of population time series data can bias the estimated effects and measures of uncertainty, if the environmental variables are autocorrelated. It is concluded that the use of state-space models is an effective way to reach more accurate results. In summary, there are several biological assumptions and methodological issues that can affect the inferential outcome when estimating environmental effects from time series data, and that therefore need special attention. The functional form of the environmental effects and potential interactions between environment and population density are important to deal with. Other issues that should be considered are assumptions about density dependent regulation, modelling potential observation error, and when needed, accounting for spatial and/or temporal autocorrelation.

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.

A robust initialization scheme for faster convergence of the dichotomous search algorithm for single frequency estimation

The estimation of the frequency of a sinusoidal signal is a well researched problem. In this work we propose an initialization scheme to the popular dichotomous search of the periodogram peak algorithm(DSPA) that is used to estimate the frequency of a sinusoid in white gaussian noise. Our initialization is computationally low cost and gives the same performance as the DSPA, while reducing the number of iterations needed for the fine search stage. We show that our algorithm remains stable as we reduce the number of iterations in the fine search stage. We also compare the performance of our modification to a previous modification of the DSPA and show that we enhance the performance of the algorithm with our initialization technique.

Country-scale carbon accounting of the vegetation and mineral soils of Finland

The increase in global temperature has been attributed to increased atmospheric concentrations of greenhouse gases (GHG), mainly that of CO2. The threat of severe and complex socio-economic and ecological implications of climate change have initiated an international process that aims to reduce emissions, to increase C sinks, and to protect existing C reservoirs. The famous Kyoto protocol is an offspring of this process. The Kyoto protocol and its accords state that signatory countries need to monitor their forest C pools, and to follow the guidelines set by the IPCC in the preparation, reporting and quality assessment of the C pool change estimates. The aims of this thesis were i) to estimate the changes in carbon stocks vegetation and soil in the forests in Finnish forests from 1922 to 2004, ii) to evaluate the applied methodology by using empirical data, iii) to assess the reliability of the estimates by means of uncertainty analysis, iv) to assess the effect of forest C sinks on the reliability of the entire national GHG inventory, and finally, v) to present an application of model-based stratification to a large-scale sampling design of soil C stock changes. The applied methodology builds on the forest inventory measured data (or modelled stand data), and uses statistical modelling to predict biomasses and litter productions, as well as a dynamic soil C model to predict the decomposition of litter. The mean vegetation C sink of Finnish forests from 1922 to 2004 was 3.3 Tg C a-1, and in soil was 0.7 Tg C a-1. Soil is slowly accumulating C as a consequence of increased growing stock and unsaturated soil C stocks in relation to current detritus input to soil that is higher than in the beginning of the period. Annual estimates of vegetation and soil C stock changes fluctuated considerably during the period, were frequently opposite (e.g. vegetation was a sink but soil was a source). The inclusion of vegetation sinks into the national GHG inventory of 2003 increased its uncertainty from between -4% and 9% to ± 19% (95% CI), and further inclusion of upland mineral soils increased it to ± 24%. The uncertainties of annual sinks can be reduced most efficiently by concentrating on the quality of the model input data. Despite the decreased precision of the national GHG inventory, the inclusion of uncertain sinks improves its accuracy due to the larger sectoral coverage of the inventory. If the national soil sink estimates were prepared by repeated soil sampling of model-stratified sample plots, the uncertainties would be accounted for in the stratum formation and sample allocation. Otherwise, the increases of sampling efficiency by stratification remain smaller. The highly variable and frequently opposite annual changes in ecosystem C pools imply the importance of full ecosystem C accounting. If forest C sink estimates will be used in practice average sink estimates seem a more reasonable basis than the annual estimates. This is due to the fact that annual forest sinks vary considerably and annual estimates are uncertain, and they have severe consequences for the reliability of the total national GHG balance. The estimation of average sinks should still be based on annual or even more frequent data due to the non-linear decomposition process that is influenced by the annual climate. The methodology used in this study to predict forest C sinks can be transferred to other countries with some modifications. The ultimate verification of sink estimates should be based on comparison to empirical data, in which case the model-based stratification presented in this study can serve to improve the efficiency of the sampling design.

Time-driven life cycle cost estimation system for product family design

This research develops a design support system, which is able to estimate the life cycle cost of different product families at the early stage of product development. By implementing the system, a designer is able to develop various cost effective product families in a shorter lead-time and minimise the destructive impact of the product family on the environment.

Subspace intersection method of high-resolution bearing estimation in shallow ocean using acoustic vector sensors

The subspace intersection method (SIM) provides unbiased bearing estimates of multiple acoustic sources in a range-independent shallow ocean using a one-dimensional search without prior knowledge of source ranges and depths. The original formulation of this method is based on deployment of a horizontal linear array of hydrophones which measure acoustic pressure. In this paper, we extend SIM to an array of acoustic vector sensors which measure pressure as well as all components of particle velocity. Use of vector sensors reduces the minimum number of sensors required by a factor of 4, and also eliminates the constraint that the intersensor spacing should not exceed half wavelength. The additional information provided by the vector sensors leads to performance enhancement in the form of lower estimation error and higher resolution.

Topology-based denoising of chaos

In this article, we propose a denoising algorithm to denoise a time series y(i) = x(i) + e(i), where {x(i)} is a time series obtained from a time- T map of a uniformly hyperbolic or Anosov flow, and {e(i)} a uniformly bounded sequence of independent and identically distributed (i.i.d.) random variables. Making use of observations up to time n, we create an estimate of x(i) for i<n. We show under typical limiting behaviours of the orbit and the recurrence properties of x(i), the estimation error converges to zero as n tends to infinity with probability 1.

The alpha method for solving differential algebraic inequality (DAI) systems

This paper describes an algorithm for ``direct numerical integration'' of the initial value Differential-Algebraic Inequalities (DAI) in a time stepping fashion using a sequential quadratic programming (SQP) method solver for detecting and satisfying active path constraints at each time step. The activation of a path constraint generally increases the condition number of the active discretized differential algebraic equation's (DAE) Jacobian and this difficulty is addressed by a regularization property of the alpha method. The algorithm is locally stable when index 1 and index 2 active path constraints and bounds are active. Subject to available regularization it is seen to be stable for active index 3 active path constraints in the numerical examples. For the high index active path constraints, the algorithm uses a user-selectable parameter to perturb the smaller singular values of the Jacobian with a view to reducing the condition number so that the simulation can proceed. The algorithm can be used as a relatively cheaper estimation tool for trajectory and control planning and in the context of model predictive control solutions. It can also be used to generate initial guess values of optimization variables used as input to inequality path constrained dynamic optimization problems. The method is illustrated with examples from space vehicle trajectory and robot path planning.

High-Rate Space-Time Coded Large-MIMO Systems: Low-Complexity Detection and Channel Estimation

In this paper, we present a low-complexity algorithm for detection in high-rate, non-orthogonal space-time block coded (STBC) large-multiple-input multiple-output (MIMO) systems that achieve high spectral efficiencies of the order of tens of bps/Hz. We also present a training-based iterative detection/channel estimation scheme for such large STBC MIMO systems. Our simulation results show that excellent bit error rate and nearness-to-capacity performance are achieved by the proposed multistage likelihood ascent search (M-LAS) detector in conjunction with the proposed iterative detection/channel estimation scheme at low complexities. The fact that we could show such good results for large STBCs like 16 X 16 and 32 X 32 STBCs from Cyclic Division Algebras (CDA) operating at spectral efficiencies in excess of 20 bps/Hz (even after accounting for the overheads meant for pilot based training for channel estimation and turbo coding) establishes the effectiveness of the proposed detector and channel estimator. We decode perfect codes of large dimensions using the proposed detector. With the feasibility of such a low-complexity detection/channel estimation scheme, large-MIMO systems with tens of antennas operating at several tens of bps/Hz spectral efficiencies can become practical, enabling interesting high data rate wireless applications.

Room temperature metathesis of aryl isocyanates and aromatic aldehydes catalyzed by group(IV) metal alkoxides: An experimental and computational study

Aromatic aldehydes and aryl isocyanates do not react at room temperature. However, we have shown for the first time that in the presence of catalytic amounts of group(IV) n-butoxide, they undergo metathesis at room temperature to produce imines with the extrusion of carbon dioxide. The mechanism of action has been investigated by a study of stoichiometric reactions. The insertion of aryl isocyanates into the metal n-butoxide occurs very rapidly. Reaction of the insertion product with the aldehyde is responsible for the metathesis. Among the n-butoxides of group(IV) metals, Ti((OBu)-Bu-n)(4) (8aTi) was found to be more efficient than Zr((OBu)-Bu-n)(4) (8aZr) and Hf((OBu)-Bu-n)(4) (8aHf) in carrying out metathesis. The surprisingly large difference in the metathetic activity of these alkoxides has been probed computationally using model complexes Ti(OMe)(4) (8bTi), Zr(OMe)(4) (8bZr) and Hf(OMe)(4) (8bHf) at the B3LYP/LANL2DZ level of theory. These studies indicate that the insertion product formed by Zr and Hf are extremely stable compared to that formed by Ti. This makes subsequent reaction of Zr and Hf complexes unfavorable.

Photocytotoxic Oxovanadium(IV) Complexes Showing Light-Induced DNA and Protein Cleavage Activity

Oxovanadium(IV) complexes [VO(L)(B)]Cl-2 (1-3), where L is bis(2-benzimidazolylmethyl)amine and B is 1,10-phenanthroline(phen),dipyrido[3,2-d:2',3'-f]quinoxaline(dpq) or dipyrido[3,2-a:2',3'-c]phenazine (dppz), have been prepared, characterized, and their photo-induced DNA and protein cleavage activity studied. The photocytotoxicity of complex 3 has been studied using adenocarcinoma A549 cells, The phen complex 1, structurally characterized by single-crystal X-ray crystallography, shows the presence of a vanadyl group in six-coordinate VON5 coordination geometry. The ligands L and phen display tridentate and bidentate N-donor chelating binding modes, respectively. The complexes exhibit a d-d band near 740 nm in 15% DMF-Tris-HCl buffer (pH 7.2). The phen and dpq complexes display an irreversible cathodic cyclic voltammetric response near -0.8 V in 20% DMF-Tris-HCl buffer having 0.1 M KCl as supporting electrolyte. The dppz complex 3 exhibits a quasi-reversible voltammogram near -0.6 V (vs SCE) that is assignable to the V(IV)-V(III)couple. The complexes bind to calf thymus DNA giving binding constant values in the range of 6.6 x 10(4)-2.9 x 10(5) M-1. The binding site size, thermal melting and viscosity binding data suggest DNA surface and/or groove binding nature of the complexes. The complexes show poor ``chemical nuclease'' activity in dark in the presence of 3-mercaptopropionic acid or hydrogen peroxide. The dpq and dppz complexes are efficient photocleavers of plasmid DNA in UV-A light of 365 nm via a mechanistic pathway that involves formation of both singlet oxygen and hydroxyl radicals. The complexes show significant photocleavage of DNA in near-IR light (>750 nm) via hydroxyl radical pathway. Among the three complexes, the dppz complex 3 shows significant BSA and lysozyme protein cleavage activity in UV-A light of 365 nm via hydroxyl radical pathway. The dppz complex 3 also exhibits photocytotoxicity in non-small cell lung carcinoma/human lung adenocarcinoma A549 cells giving IC50 value of 17 mu M in visible light(IC50 = 175 mu M in dark).

Long-term Outcome of DSM-IV Major Depressive Disorder in Psychiatric Care

Much of what we know regarding the long-term course and outcome of major depressive disorder (MDD) is based on studies of mostly inpatient tertiary level cohorts and samples predating the era of the current antidepressants and the use of maintenance therapies. In addition, there is a lack of studies investigating the comprehensive significance of comorbid axis I and II disorders on the outcome of MDD. The present study forms a part of the Vantaa Depression Study (VDS), a regionally representative prospective and naturalistic cohort study of 269 secondary-level care psychiatric out- and inpatients (aged 20-59) with a new episode of DSM-IV MDD, and followed-up up to five years (n=182) with a life-chart and semistructured interviews. The aim was to investigate the long-term outcome of MDD and risk factors for poor recovery, recurrences, suicidal attempts and diagnostic switch to bipolar disorder, and the association of a family history of different psychiatric disorders on the outcome. The effects of comorbid disorders together with various other predictors from different domains on the outcome were comprehensively investigated. According to this study, the long-term outcome of MDD appears to be more variable when its outcome is investigated among modern, community-treated, secondary-care outpatients compared to previous mostly inpatient studies. MDD was also highly recurrent in these settings, but the recurrent episodes seemed shorter, and the outcome was unlikely to be uniformly chronic. Higher severity of MDD predicted significantly the number of recurrences and longer time spent ill. In addition, longer episode duration, comorbid dysthymic disorder, cluster C personality disorders and social phobia predicted a worse outcome. The incidence rate of suicide attempts varied robustly de¬pending on the level of depression, being 21-fold during major depressive episodes (MDEs), and 4-fold during partial remission compared to periods of full remission. Although a history of previous attempts and poor social support also indicated risk, time spent depressed was the central factor determining overall long-term risk. Switch to bipolar disorder occurred mainly to type II, earlier to type I, and more gradually over time to type II. Higher severity of MDD, comorbid social phobia, obsessive compulsive disorder, and cluster B personality disorder features predicted the diagnostic switch. The majority of patients were also likely to have positive family histories not exclusively of mood, but also of other mental disorders. Having a positive family history of severe mental disorders was likely to be clinically associated with a significantly more adverse outcome.

Recognition, comorbidity, and outcome of DSM-IV bipolar I and II disorders in psychiatric care

This study is part of an ongoing collaborative bipolar research project, the Jorvi Bipolar Study (JoBS). The JoBS is run by the Department of Mental Health and Alcohol Research of the National Public Health Institute, Helsinki, and the Department of Psychiatry, Jorvi Hospital, Helsinki University Central Hospital (HUCH), Espoo, Finland. It is a prospective, naturalistic cohort study of secondary level care psychiatric in- and outpatients with a new episode of bipolar disorder (BD). The second report also included 269 major depressive disorder (MDD) patients from the Vantaa Depression Study (VDS). The VDS was carried out in collaboration with the Department of Psychiatry of the Peijas Medical Care District. Using the Mood Disorder Questionnaire (MDQ), all in- and outpatients at the Department of Psychiatry at Jorvi Hospital who currently had a possible new phase of DSM-IV BD were sought. Altogether, 1630 psychiatric patients were screened, and 490 were interviewed using a semistructured interview (SCID-I/P). The patients included in the cohort (n=191) had at intake a current phase of BD. The patients were evaluated at intake and at 6- and 18-month interviews. Based on this study, BD is poorly recognized even in psychiatric settings. Of the BD patients with acute worsening of illness, 39% had never been correctly diagnosed. The classic presentations of BD with hospitalizations, manic episodes, and psychotic symptoms lead clinicians to correct diagnosis of BD I in psychiatric care. Time of follow-up elapsed in psychiatric care, but none of the clinical features, seemed to explain correct diagnosis of BD II, suggesting reliance on cross- sectional presentation of illness. Even though BD II was clearly less often correctly diagnosed than BD I, few other differences between the two types of BD were detected. BD I and II patients appeared to differ little in terms of clinical picture or comorbidity, and the prevalence of psychiatric comorbidity was strongly related to the current illness phase in both types. At the same time, the difference in outcome was clear. BD II patients spent about 40% more time depressed than BD I patients. Patterns of psychiatric comorbidity of BD and MDD differed somewhat qualitatively. Overall, MDD patients were likely to have more anxiety disorders and cluster A personality disorders, and bipolar patients to have more cluster B personality disorders. The adverse consequences of missing or delayed diagnosis are potentially serious. Thus, these findings strongly support the value of screening for BD in psychiatric settings, especially among the major depressive patients. Nevertheless, the diagnosis must be based on a clinical interview and follow-up of mood. Comorbidity, present in 59% of bipolar patients in a current phase, needs concomitant evaluation, follow-up, and treatment. To improve outcome in BD, treatment of bipolar depression is a major challenge for clinicians.

Tumour markers as prognostic factors of survival of gastric cancer patients

The incidence of gastric cancer in the last decades has declined rapidly in the industrialised countries. Worldwide, however, gastric cancer is still the second most common cause of cancer death. Although surgery is currently the most effective treatment, the rapid progress in adjuvant chemotherapy and radiation therapy requires a re-evaluation of prognosis assessment. The TNM staging system of the UICC is ubiquitously used; it groups patients by decreasing survival times from stage I to stage IV based on the spread of disease, i.e. depth of tumour penetration (T), extent of spread to lymph nodes (N), and the presence or absence of distant (M) metastases. This is by far the most consistent prognostic classification system today. However, even within the stage groups there are patients that follow a varying course of disease. Our knowledge of the molecular differences between tumours of the same stage and morphology has been accumulating over the years and methods for a more accurate assessment of the phenotype of neoplasias are of value when evaluating the prognosis of individual patients with gastric cancer. In this study, the immunohistochemical expression of tumour markers involved in different phases in tumourigenesis was examined. The aim was to find new markers which could provide prognostic information in addition to what is provided by the TNM variables. A total of 337 specimens from the primary tumour of patients who underwent surgery for gastric cancer were collected and the immunohistochemical expression of seven different biomarkers was analysed. DNA ploidy and S-phase fraction (SPF) was assessed by flow cytometry. Finally, all biomarkers and clinicopathological prognostic factors were combined and evaluated by a multivariate Cox regression model to elucidate which specific factors provide independent prognostic information. By univariate survival analysis the following variables were significant prognostic factors: epithelial and stromal syndecan-1 expression, stromal tenascin-C expression, expression of tumour-associated trypsin inhibitor (TATI) in cancer cells, nuclear p53 expression, nuclear p21 expression, DNA ploidy, and SPF. By multivariate survival analysis adjusted for all available clinicopathological and biomolecular variables, p53 expression, p21 expression, and DNA ploidy emerged as independent prognostic biomarkers, together with penetration depth of the tumour, presence of nodal metastases, surgical cure of the cancer, and age of the patient at the time of diagnosis.