Spring Phenology of Butterflies : The role of seasonal variation in life-cycle regulation

Animals and plants in temperate regions must adapt their life cycle to pronounced seasonal variation. The research effort that has gone into studying these cyclical life history events, or phenological traits, has increased greatly in recent decades. As phenological traits are often correlated to temperature, they are relevant to study in terms of understanding the effect of short term environmental variation as well as long term climate change. Because of this, changes in phenology are the most obvious and among the most commonly reported responses to climate change. Moreover, phenological traits are important for fitness as they determine the biotic and abiotic environment an individual encounters. Fine-tuning of phenology allows for synchronisation at a local scale to mates, food resources and appropriate weather conditions. On a between-population scale, variation in phenology may reflect regional variation in climate. Such differences can not only give insights to life cycle adaptation, but also to how populations may respond to environmental change through time. This applies both on an ecological scale through phenotypic plasticity as well as an evolutionary scale through genetic adaptation. In this thesis I have used statistical and experimental methods to investigate both the larger geographical patterns as well as mechanisms of fine-tuning of phenology of several butterfly species. The main focus, however, is on the orange tip butterfly, Anthocharis cardamines, in Sweden and the United Kingdom. I show a contrasting effect of spring temperature and winter condition on spring phenology for three out of the five studied butterfly species. For A. cardamines there are population differences in traits responding to these environmental factors between and within Sweden and the UK that suggest adaptation to local environmental conditions. All populations show a strong negative plastic relationship between spring temperature and spring phenology, while the opposite is true for winter cold duration. Spring phenology is shifted earlier with increasing cold duration. The environmental variables show correlations, for example, during a warm year a short winter delays phenology while a warm spring speeds phenology up. Correlations between the environmental variables also occur through space, as the locations that have long winters also have cold springs. The combined effects of these two environmental variables cause a complex geographical pattern of phenology across the UK and Sweden. When predicting phenology with future climate change or interpreting larger geographical patterns one must therefore have a good enough understanding of how the phenology is controlled and take the relevant environmental factors in to account. In terms of the effect of phenological change, it should be discussed with regards to change in life cycle timing among interacting species. For example, the phenology of the host plants is important for A. cardamines fitness, and it is also the main determining factor for oviposition. In summary, this thesis shows that the broad geographical pattern of phenology of the butterflies is formed by counteracting environmental variables, but that there also are significant population differences that enable fine-tuning of phenology according to the seasonal progression and variation at the local scale.

Theoretical studies of Membrane Proteins : Properties, Prediction Methods and Genome-wide analysis

Membrane proteins are a large and important class of proteins. They are responsible for several of the key functions in a living cell, e.g. transport of nutrients and ions, cell-cell signaling, and cell-cell adhesion. Despite their importance it has not been possible to study their structure and organization in much detail because of the difficulty to obtain 3D structures. In this thesis theoretical studies of membrane protein sequences and structures have been carried out by analyzing existing experimental data. The data comes from several sources including sequence databases, genome sequencing projects, and 3D structures. Prediction of the membrane spanning regions by hydrophobicity analysis is a key technique used in several of the studies. A novel method for this is also presented and compared to other methods. The primary questions addressed in the thesis are: What properties are common to all membrane proteins? What is the overall architecture of a membrane protein? What properties govern the integration into the membrane? How many membrane proteins are there and how are they distributed in different organisms? Several of the findings have now been backed up by experiments. An analysis of the large family of G-protein coupled receptors pinpoints differences in length and amino acid composition of loops between proteins with and without a signal peptide and also differences between extra- and intracellular loops. Known 3D structures of membrane proteins have been studied in terms of hydrophobicity, distribution of secondary structure and amino acid types, position specific residue variability, and differences between loops and membrane spanning regions. An analysis of several fully and partially sequenced genomes from eukaryotes, prokaryotes, and archaea has been carried out. Several differences in the membrane protein content between organisms were found, the most important being the total number of membrane proteins and the distribution of membrane proteins with a given number of transmembrane segments. Of the properties that were found to be similar in all organisms, the most obvious is the bias in the distribution of positive charges between the extra- and intracellular loops. Finally, an analysis of homologues to membrane proteins with known topology uncovered two related, multi-spanning proteins with opposite predicted orientations. The predicted topologies were verified experimentally, providing a first example of "divergent topology evolution".

Multivariate spectroscopic methods for the analysis of solutions

In this thesis some multivariate spectroscopic methods for the analysis of solutions are proposed. Spectroscopy and multivariate data analysis form a powerful combination for obtaining both quantitative and qualitative information and it is shown how spectroscopic techniques in combination with chemometric data evaluation can be used to obtain rapid, simple and efficient analytical methods. These spectroscopic methods consisting of spectroscopic analysis, a high level of automation and chemometric data evaluation can lead to analytical methods with a high analytical capacity, and for these methods, the term high-capacity analysis (HCA) is suggested. It is further shown how chemometric evaluation of the multivariate data in chromatographic analyses decreases the need for baseline separation. The thesis is based on six papers and the chemometric tools used are experimental design, principal component analysis (PCA), soft independent modelling of class analogy (SIMCA), partial least squares regression (PLS) and parallel factor analysis (PARAFAC). The analytical techniques utilised are scanning ultraviolet-visible (UV-Vis) spectroscopy, diode array detection (DAD) used in non-column chromatographic diode array UV spectroscopy, high-performance liquid chromatography with diode array detection (HPLC-DAD) and fluorescence spectroscopy. The methods proposed are exemplified in the analysis of pharmaceutical solutions and serum proteins. In Paper I a method is proposed for the determination of the content and identity of the active compound in pharmaceutical solutions by means of UV-Vis spectroscopy, orthogonal signal correction and multivariate calibration with PLS and SIMCA classification. Paper II proposes a new method for the rapid determination of pharmaceutical solutions by the use of non-column chromatographic diode array UV spectroscopy, i.e. a conventional HPLC-DAD system without any chromatographic column connected. In Paper III an investigation is made of the ability of a control sample, of known content and identity to diagnose and correct errors in multivariate predictions something that together with use of multivariate residuals can make it possible to use the same calibration model over time. In Paper IV a method is proposed for simultaneous determination of serum proteins with fluorescence spectroscopy and multivariate calibration. Paper V proposes a method for the determination of chromatographic peak purity by means of PCA of HPLC-DAD data. In Paper VI PARAFAC is applied for the decomposition of DAD data of some partially separated peaks into the pure chromatographic, spectral and concentration profiles.

Variance Reduction in Analytical Chemistry : New Numerical Methods in Chemometrics and Molecular Simulation

This thesis is based on five papers addressing variance reduction in different ways. The papers have in common that they all present new numerical methods. Paper I investigates quantitative structure-retention relationships from an image processing perspective, using an artificial neural network to preprocess three-dimensional structural descriptions of the studied steroid molecules. Paper II presents a new method for computing free energies. Free energy is the quantity that determines chemical equilibria and partition coefficients. The proposed method may be used for estimating, e.g., chromatographic retention without performing experiments. Two papers (III and IV) deal with correcting deviations from bilinearity by so-called peak alignment. Bilinearity is a theoretical assumption about the distribution of instrumental data that is often violated by measured data. Deviations from bilinearity lead to increased variance, both in the data and in inferences from the data, unless invariance to the deviations is built into the model, e.g., by the use of the method proposed in paper III and extended in paper IV. Paper V addresses a generic problem in classification; namely, how to measure the goodness of different data representations, so that the best classifier may be constructed. Variance reduction is one of the pillars on which analytical chemistry rests. This thesis considers two aspects on variance reduction: before and after experiments are performed. Before experimenting, theoretical predictions of experimental outcomes may be used to direct which experiments to perform, and how to perform them (papers I and II). After experiments are performed, the variance of inferences from the measured data are affected by the method of data analysis (papers III-V).