TY - JOUR A1 - Berner, Nadine A1 - Trauth, Martin H. A1 - Holschneider, Matthias T1 - Bayesian inference about Plio-Pleistocene climate transitions in Africa JF - Quaternary science reviews : the international multidisciplinary research and review journal N2 - During the last 5 Ma the Earth's ocean-atmosphere system passed through several major transitions, many of which are discussed as possible triggers for human evolution. A classic in this context is the possible influence of the closure of the Panama Strait, the intensification of Northern Hemisphere Glaciation, a stepwise increase in aridity in Africa, and the first appearance of the genus Homo about 2.5 - 2.7 Ma ago. Apart from the fact that the correlation between these events does not necessarily imply causality, many attempts to establish a relationship between climate and evolution fail due to the challenge of precisely localizing an a priori unknown number of changes potentially underlying complex climate records. The kernel-based Bayesian inference approach applied here allows inferring the location, generic shape, and temporal scale of multiple transitions in established records of Plio-Pleistocene African climate. By defining a transparent probabilistic analysis strategy, we are able to identify conjoint changes occurring across the investigated terrigenous dust records from Ocean Drilling Programme (ODP) sites in the Atlantic Ocean (ODP 659), Arabian (ODP 721/722) and Mediterranean Sea (ODP 967). The study indicates a two-step transition in the African climate proxy records at (2.35-2.10) Ma and (1.70 - 1.50) Ma, that may be associated with the reorganization of the Hadley-Walker Circulation. . KW - Plio-Pleistocene KW - Hadley-Walker Circulation KW - climate transition KW - Bayesian inference KW - time series analysis KW - ODP 659 KW - ODP 721/722 KW - ODP 967 Y1 - 2022 U6 - https://doi.org/10.1016/j.quascirev.2021.107287 SN - 0277-3791 SN - 1873-457X VL - 277 PB - Elsevier CY - Oxford ER - TY - THES A1 - Berner, Nadine T1 - Deciphering multiple changes in complex climate time series using Bayesian inference T1 - Bayes'sche Inferenz als diagnostischer Ansatz zur Untersuchung multipler Übergänge in komplexen Klimazeitreihen N2 - 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. N2 - Im Allgemeinen stellen punktuelle Veränderungen in Zeitreihen (change points) eine Heterogenität in den statistischen oder dynamischen Charakteristika der Observablen dar. Das Auffinden und die Beschreibung solcher Übergänge bietet grundlegende Informationen über das beobachtete System hinsichtlich seiner intrinsischen Entwicklung sowie potentieller externer Einflüsse. Eine präzise Detektion von Veränderungen ist daher für die verschiedensten Forschungsgebiete, wie den Umweltwissenschaften, der Bioinformatik und den Wirtschaftswissenschaften von großem Interesse. Die primäre Zielsetzung der in der vorliegenden Doktorarbeit vorgestellten Detektionsmethode ist die Untersuchung von direkten als auch indirekten Klimaobservablen auf Veränderungen. Um die damit verbundene Vielzahl an möglichen natürlichen Prozessen zu beschreiben, werden im Rahmen einer Bayes’schen Inversion die generischen statistischen Merkmale Zentraltendenz und Dispersion verwendet. Im Gegensatz zu etablierten Bayes’schen Methoden zur Analyse von multiplen Übergängen, die im Rahmen von Partitionsmodellen hoher Dimensionalität formuliert sind und die Spezifikation von Priorverteilungen erfordern, wird in dieser Doktorarbeit ein generischer, Kernel-basierter Ansatz niedriger Dimensionalität mit minimal informativen Priorverteilungen vorgestellt. Zunächst wird ein lokaler Bayes’scher Inversionsansatz entwickelt, der robuste Rückschlüsse auf die Position und die generischen Charakteristika einer einzelnen Veränderung erlaubt. Durch die Analyse von synthetischen Zeitreihen die dem Einfluss von Veränderungen unterschiedlicher Signifikanz, Datenverlust und Ausreißern unterliegen wird die Leistungsfähigkeit, Konsistenz und Sensitivität der Inversionmethode begründet. Um Zeitreihen auch auf multiple Veränderungen systematisch untersuchen zu können, wird die Methode der Bayes’schen Inversion zu einem Kernel-basierten Ansatz erweitert. Durch die Einführung grundlegender Kernel-Maße können die Kernel-Resultate zu einer gewichteten Wahrscheinlichkeit kombiniert werden die als Proxy einer Posterior-Verteilung multipler Veränderungen dient. Der Detektionsalgorithmus wird auf reale Umweltmessreihen vom Nil-Fluss in Aswan und von der Wetterstation Tuscaloosa, Alabama, angewendet, die jeweils dokumentierte Veränderungen enthalten. Das Ergebnis dieser Analyse bestätigt den entwickelten Ansatz als eine leistungsstarke diagnostische Methode zur Detektion multipler Übergänge in Zeitreihen. Abschließend wird der generische Kernel-basierte Bayes’sche Ansatz verwendet, um eine Reihe von komplexen terrigenen Staubdaten zu untersuchen, die als Klimaindikatoren der afrikanischen Region des Plio-Pleistozän interpretiert werden. Eine detaillierte Untersuchung deutet auf multiple Veränderungen in den indirekten Klimaobservablen hin, von denen einige als gemeinsame Übergänge interpretiert werden. Diese gemeinsam auftretenden Ereignisse stimmen mit etablierten globalen Klimaereignissen überein. Insbesondere der gefundene Zwei-Stufen-Übergang, der mit der Ausbildung der modernen Walker-Zirkulation assoziiert wird, liefert einen wichtigen Beitrag zur aktuellen Diskussion über den Einfluss von paläoklimatischen Veränderungen auf die Umweltbedingungen im tropischen und subtropischen Afrika vor circa zwei Millionen Jahren. KW - kernel-based Bayesian inference KW - multi-change point detection KW - direct and indirect climate observations KW - Plio-Pleistocene KW - (sub-) tropical Africa KW - terrigenous dust KW - kernel-basierte Bayes'sche Inferenz KW - Detektion multipler Übergänge KW - direkte und indirekte Klimaobservablen KW - Plio-Pleistozän KW - (sub-) tropisches Afrika KW - terrigener Staub Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-100065 ER - TY - JOUR A1 - Birkhofer, Klaus A1 - Schöning, Ingo A1 - Alt, Fabian A1 - Herold, Nadine A1 - Klarner, Bernhard A1 - Maraun, Mark A1 - Marhan, Sven A1 - Oelmann, Yvonne A1 - Wubet, Tesfaye A1 - Yurkov, Andrey A1 - Begerow, Dominik A1 - Berner, Doreen A1 - Buscot, Francois A1 - Daniel, Rolf A1 - Diekötter, Tim A1 - Ehnes, Roswitha B. A1 - Erdmann, Georgia A1 - Fischer, Christiane A1 - Fösel, Baerbel A1 - Groh, Janine A1 - Gutknecht, Jessica A1 - Kandeler, Ellen A1 - Lang, Christa A1 - Lohaus, Gertrud A1 - Meyer, Annabel A1 - Nacke, Heiko A1 - Näther, Astrid A1 - Overmann, Jörg A1 - Polle, Andrea A1 - Pollierer, Melanie M. A1 - Scheu, Stefan A1 - Schloter, Michael A1 - Schulze, Ernst-Detlef A1 - Schulze, Waltraud X. A1 - Weinert, Jan A1 - Weisser, Wolfgang W. A1 - Wolters, Volkmar A1 - Schrumpf, Marion T1 - General relationships between abiotic soil properties and soil biota across spatial scales and different land-use types JF - PLoS one N2 - Very few principles have been unraveled that explain the relationship between soil properties and soil biota across large spatial scales and different land-use types. Here, we seek these general relationships using data from 52 differently managed grassland and forest soils in three study regions spanning a latitudinal gradient in Germany. We hypothesize that, after extraction of variation that is explained by location and land-use type, soil properties still explain significant proportions of variation in the abundance and diversity of soil biota. If the relationships between predictors and soil organisms were analyzed individually for each predictor group, soil properties explained the highest amount of variation in soil biota abundance and diversity, followed by land-use type and sampling location. After extraction of variation that originated from location or land-use, abiotic soil properties explained significant amounts of variation in fungal, meso-and macrofauna, but not in yeast or bacterial biomass or diversity. Nitrate or nitrogen concentration and fungal biomass were positively related, but nitrate concentration was negatively related to the abundances of Collembola and mites and to the myriapod species richness across a range of forest and grassland soils. The species richness of earthworms was positively correlated with clay content of soils independent of sample location and land-use type. Our study indicates that after accounting for heterogeneity resulting from large scale differences among sampling locations and land-use types, soil properties still explain significant proportions of variation in fungal and soil fauna abundance or diversity. However, soil biota was also related to processes that act at larger spatial scales and bacteria or soil yeasts only showed weak relationships to soil properties. We therefore argue that more general relationships between soil properties and soil biota can only be derived from future studies that consider larger spatial scales and different land-use types. Y1 - 2012 U6 - https://doi.org/10.1371/journal.pone.0043292 SN - 1932-6203 VL - 7 IS - 8 PB - PLoS CY - San Fransisco ER -