East-West Genetic Differentiation in Musk Ducks (Biziura lobata) of Australia Suggests Late Pleistocene Divergence at the Nullarbor Plain

Musk Ducks (Biziura lobata) are endemic to Australia and occur as two geographically isolated populations separated by the Nullarbor Plain, a vast arid region in southern Australia. We studied genetic variation in Musk Duck populations at coarse (eastern versus western Australia) and fine scales (four sites within eastern Australia). We found significant genetic structure between eastern and western Australia in the mtDNA control region (UST = 0.747), one nuclear intron (UST = 0.193) and eight microsatellite loci (FST = 0.035). In contrast, there was little genetic structure between Kangaroo Island and adjacent mainland regions within eastern Australia. One small population of Musk Ducks in Victoria (Lake Wendouree) differed from both Kangaroo Island and the remainder of mainland eastern Australia, possibly due to genetic drift exacerbated by inbreeding and small population size. The observed low pairwise distance between the eastern and western mtDNA lineages (0.36%) suggests that they diverged near the end of the Pleistocene, a period characterised by frequent shifts between wet and arid conditions in central Australia. Our genetic results corroborate the display call divergence and Mathews’ (Austral Avian Record 2:83–107, 1914) subspecies classification, and confirm that eastern and western populations of Musk Duck are currently isolated from each other

Bayesian tests on components of the compound symmetry covariance matrix

Complex dependency structures are often conditionally modeled, where random effects parameters are used to specify the natural heterogeneity in the population. When interest is focused on the dependency structure, inferences can be made from a complex covariance matrix using a marginal modeling approach. In this marginal modeling framework, testing covariance parameters is not a boundary problem. Bayesian tests on covariance parameter(s) of the compound symmetry structure are proposed assuming multivariate normally distributed observations. Innovative proper prior distributions are introduced for the covariance components such that the positive definiteness of the (compound symmetry) covariance matrix is ensured. Furthermore, it is shown that the proposed priors on the covariance parameters lead to a balanced Bayes factor, in case of testing an inequality constrained hypothesis. As an illustration, the proposed Bayes factor is used for testing (non-)invariant intra-class correlations across different group types (public and Catholic schools), using the 1982 High School and Beyond survey data

Bayes Factor Covariance Testing in Item Response Models

Two marginal one-parameter item response theory models are introduced, by integrating out the latent variable or random item parameter. It is shown that both marginal response models are multivariate (probit) models with a compound symmetry covariance structure. Several common hypotheses concerning the underlying covariance structure are evaluated using (fractional) Bayes factor tests. The support for a unidimensional factor (i.e., assumption of local independence) and differential item functioning are evaluated by testing the covariance components. The posterior distribution of common covariance components is obtained in closed form by transforming latent responses with an orthogonal (Helmert) matrix. This posterior distribution is defined as a shifted-inverse-gamma, thereby introducing a default prior and a balanced prior distribution. Based on that, an MCMC algorithm is described to estimate all model parameters and to compute (fractional) Bayes factor tests. Simulation studies are used to show that the (fractional) Bayes factor tests have good properties for testing the underlying covariance structure of binary response data. The method is illustrated with two real data studies

Raising in Turkish

Proceedings of the 2nd Annual Meeting of the Berkeley Linguistics Society (1976), pp. 298-30

Complex Latent Variable Modeling in Educational Assessment

Bayesian item response theory models have been widely used in different research fields. They support measuring constructs and modeling relationships between constructs, while accounting for complex test situations (e.g., complex sampling designs, missing data, heterogenous population). Advantages of this flexible modeling framework together with powerful simulation-based estimation techniques are discussed. Furthermore, it is shown how the Bayes factor can be used to test relevant hypotheses in assessment using the College Basic Academic Subjects Examination (CBASE) data

Variation at the DRD4 locus is associated with wariness and local site selection in urban black swans

BACKGROUND: Interactions between wildlife and humans are increasing. Urban animals are often less wary of humans than their non-urban counterparts, which could be explained by habituation, adaptation or local site selection. Under local site selection, individuals that are less tolerant of humans are less likely to settle in urban areas. However, there is little evidence for such temperament-based site selection, and even less is known about its underlying genetic basis. We tested whether site selection in urban and non-urban habitats by black swans (Cygnus atratus) was associated with polymorphisms in two genes linked to fear in animals, the dopamine receptor D4 (DRD4) and serotonin transporter (SERT) genes. RESULTS: Wariness in swans was highly repeatable between disturbance events (repeatability = 0.61) and non-urban swans initiated escape from humans earlier than urban swans. We found no inter-individual variation in the SERT gene, but identified five DRD4 genotypes and an association between DRD4 genotype and wariness. Individuals possessing the most common DRD4 genotype were less wary than individuals possessing rarer genotypes. As predicted by the local site selection hypothesis, genotypes associated with wary behaviour were over three times more frequent at the non-urban site. This resulted in moderate population differentiation at DRD4 (FST = 0.080), despite the sites being separated by only 30 km, a short distance for this highly-mobile species. Low population differentiation at neutrally-selected microsatellite loci and the likely occasional migration of swans between the populations reduces the likelihood of local site adaptations. CONCLUSION: Our results suggest that wariness in swans is partly genetically-determined and that wary swans settle in less-disturbed areas. More generally, our findings suggest that site-specific management strategies may be necessary that consider the temperament of local animals