Metodi molecolari nello studio della variabilità genetica di Tuber borchii

Tuber borchii (Ascomycota, order Pezizales) is highly valued truffle sold in local markets in Italy. Despite its economic importance, knowledge on its distribution and population variation is scarce. The objective of this work was to investigate the evolutionary forces shaping the genetic structure of this fungus using coalescent and phylogenetic methods to reconstruct the evolutionary history of populations in Italy. To assess population structure, 61 specimens were collected from 11 different Provinces of Italy. Sampling was stratified across hosts and habitats to maximize coverage in native oak and pine stands and both mychorrizae and fruiting bodies were collected. Samples were identified considering anatomo-morphological characters. DNA was extracted and both multilocus (AFLP) and single-locus (18 loci from rDNA, nDNA, and mtDNA) approaches were used to look for polymorphisms. Screening AFLP profiles, both Jaccard and Dice coefficients of similarity were utilized to transform binary matrix into a distance matrix and then to desume Neighbour-Joining trees. Though these are only preliminary examinations, phylogenetic trees were totally concordant with those deriving from single locus analyses. Phylogenetic analyses of the nuclear loci were performed using maximum likelihood with PAUP and a combined phylogenetic inference, using Bayesian estimation with all nuclear gene regions, was carried out. To reconstruct the evolutionary history, we estimated recurrent migration, migration across the history of the sample, and estimated the mutation and approximate age of mutations in each tree using SNAP Workbench. The combined phylogenetic tree using Bayesian estimation suggests that there are two main haplotypes that are difficult to be differentiated on the basis of morphology, of ecological parameters and symbiontic tree. Between these two lineages, that occur in sympatry within T. borchii populations, there is no evidence of recurrent migration. However, migration over the history of the sample was asymmetrical suggesting that isolation was a result of interrupted gene flow followed by range expansion. Low levels of divergence between the haplotypes indicate that there are likely to be two cryptic species within the T. borchii population sampled. Our results suggest that isolation between populations of T. borchii could have led to reproductive isolation between two lineages. This isolation is likely due to sympatric speciation caused by a multiple colonization from different refugia or a recent isolation. In attempting to determinate whether these haplotypes represent separate species or a partition of the same species we applied Biological and Mechanistic species Concepts. Notwithstanding, further analyses are necessary to evaluate if selection favoured premating or post-mating isolation.

Assessing the fit of unidimensional IRT models for binary data under model misspecification

Model misspecification affects the classical test statistics used to assess the fit of the Item Response Theory (IRT) models. Robust tests have been derived under model misspecification, as the Generalized Lagrange Multiplier and Hausman tests, but their use has not been largely explored in the IRT framework. In the first part of the thesis, we introduce the Generalized Lagrange Multiplier test to detect differential item response functioning in IRT models for binary data under model misspecification. By means of a simulation study and a real data analysis, we compare its performance with the classical Lagrange Multiplier test, computed using the Hessian and the cross-product matrix, and the Generalized Jackknife Score test. The power of these tests is computed empirically and asymptotically. The misspecifications considered are local dependence among items and non-normal distribution of the latent variable. The results highlight that, under mild model misspecification, all tests have good performance while, under strong model misspecification, the performance of the tests deteriorates. None of the tests considered show an overall superior performance than the others. In the second part of the thesis, we extend the Generalized Hausman test to detect non-normality of the latent variable distribution. To build the test, we consider a seminonparametric-IRT model, that assumes a more flexible latent variable distribution. By means of a simulation study and two real applications, we compare the performance of the Generalized Hausman test with the M2 limited information goodness-of-fit test and the Likelihood-Ratio test. Additionally, the information criteria are computed. The Generalized Hausman test has a better performance than the Likelihood-Ratio test in terms of Type I error rates and the M2 test in terms of power. The performance of the Generalized Hausman test and the information criteria deteriorates when the sample size is small and with a few items.