@phdthesis{Berner2016,
  author    = {Berner, Nadine},
  title     = {Deciphering multiple changes in complex climate time series using Bayesian inference},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-100065},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xvi, 135},
  year      = {2016},
  abstract  = {Change points in time series are perceived as heterogeneities in the statistical or dynamical characteristics of the observations. Unraveling such transitions yields essential information for the understanding of the observed system's intrinsic evolution and potential external influences. A precise detection of multiple changes is therefore of great importance for various research disciplines, such as environmental sciences, bioinformatics and economics. The primary purpose of the detection approach introduced in this thesis is the investigation of transitions underlying direct or indirect climate observations. In order to develop a diagnostic approach capable to capture such a variety of natural processes, the generic statistical features in terms of central tendency and dispersion are employed in the light of Bayesian inversion. In contrast to established Bayesian approaches to multiple changes, the generic approach proposed in this thesis is not formulated in the framework of specialized partition models of high dimensionality requiring prior specification, but as a robust kernel-based approach of low dimensionality employing least informative prior distributions. First of all, a local Bayesian inversion approach is developed to robustly infer on the location and the generic patterns of a single transition. The analysis of synthetic time series comprising changes of different observational evidence, data loss and outliers validates the performance, consistency and sensitivity of the inference algorithm. To systematically investigate time series for multiple changes, the Bayesian inversion is extended to a kernel-based inference approach. By introducing basic kernel measures, the weighted kernel inference results are composed into a proxy probability to a posterior distribution of multiple transitions. The detection approach is applied to environmental time series from the Nile river in Aswan and the weather station Tuscaloosa, Alabama comprising documented changes. The method's performance confirms the approach as a powerful diagnostic tool to decipher multiple changes underlying direct climate observations. Finally, the kernel-based Bayesian inference approach is used to investigate a set of complex terrigenous dust records interpreted as climate indicators of the African region of the Plio-Pleistocene period. A detailed inference unravels multiple transitions underlying the indirect climate observations, that are interpreted as conjoint changes. The identified conjoint changes coincide with established global climate events. In particular, the two-step transition associated to the establishment of the modern Walker-Circulation contributes to the current discussion about the influence of paleoclimate changes on the environmental conditions in tropical and subtropical Africa at around two million years ago.},
  language  = {en}
}
@article{GaubertPatelVeronetal.2016,
  author    = {Gaubert, Philippe and Patel, Riddhi P. and Veron, Geraldine and Goodman, Steven M. and Willsch, Maraike and Vasconcelos, Raquel and Lourenco, Andre and Sigaud, Marie and Justy, Fabienne and Joshi, Bheem Dutt and Fickel, J{\"o}rns and Wilting, Andreas},
  title     = {Phylogeography of the Small Indian Civet and Origin of Introductions to Western Indian Ocean Islands},
  series = {The journal of heredity : official journal of the American Genetic Association},
  volume    = {108},
  journal   = {The journal of heredity : official journal of the American Genetic Association},
  publisher = {Oxford Univ. Press},
  address   = {Cary},
  issn      = {0022-1503},
  doi       = {10.1093/jhered/esw085},
  pages     = {270 -- 279},
  year      = {2016},
  abstract  = {The biogeographic dynamics affecting the Indian subcontinent, East and Southeast Asia during the Plio-Pleistocene has generated complex biodiversity patterns. We assessed the molecular biogeography of the small Indian civet (Viverricula indica) through mitogenome and cytochrome b + control region sequencing of 89 historical and modern samples to (1) establish a time-calibrated phylogeography across the species' native range and (2) test introduction scenarios to western Indian Ocean islands. Bayesian phylogenetic analyses identified 3 geographic lineages (East Asia, sister-group to Southeast Asia and the Indian subcontinent + northern Indochina) diverging 3.2-2.3 million years ago (Mya), with no clear signature of past demographic expansion. Within Southeast Asia, Balinese populations separated from the rest 2.6-1.3 Mya. Western Indian Ocean populations were assigned to the Indian subcontinent + northern Indochina lineage and had the lowest mitochondrial diversity. Approximate Bayesian computation did not distinguish between single versus multiple introduction scenarios. The early diversification of the small Indian civet was likely shaped by humid periods in the Late Pliocene-Early Pleistocene that created evergreen rainforest barriers, generating areas of intra-specific endemism in the Indian subcontinent, East, and Southeast Asia. Later, Pleistocene dispersals through drier conditions in South and Southeast Asia were likely, giving rise to the species' current natural distribution. Our molecular data supported the delineation of only 4 subspecies in V. indica, including an endemic Balinese lineage. Our study also highlighted the influence of prefirst millennium AD introductions to western Indian Ocean islands, with Indian and/or Arab traders probably introducing the species for its civet oil.},
  language  = {en}
}