@article{BernerTrauthHolschneider2022,
  author    = {Berner, Nadine and Trauth, Martin H. and Holschneider, Matthias},
  title     = {Bayesian inference about Plio-Pleistocene climate transitions in Africa},
  series = {Quaternary science reviews : the international multidisciplinary research and review journal},
  volume    = {277},
  journal   = {Quaternary science reviews : the international multidisciplinary research and review journal},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0277-3791},
  doi       = {10.1016/j.quascirev.2021.107287},
  pages     = {12},
  year      = {2022},
  abstract  = {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. .},
  language  = {en}
}
@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{BirkhoferSchoeningAltetal.2012,
  author    = {Birkhofer, Klaus and Sch{\"o}ning, Ingo and Alt, Fabian and Herold, Nadine and Klarner, Bernhard and Maraun, Mark and Marhan, Sven and Oelmann, Yvonne and Wubet, Tesfaye and Yurkov, Andrey and Begerow, Dominik and Berner, Doreen and Buscot, Francois and Daniel, Rolf and Diek{\"o}tter, Tim and Ehnes, Roswitha B. and Erdmann, Georgia and Fischer, Christiane and F{\"o}sel, Baerbel and Groh, Janine and Gutknecht, Jessica and Kandeler, Ellen and Lang, Christa and Lohaus, Gertrud and Meyer, Annabel and Nacke, Heiko and N{\"a}ther, Astrid and Overmann, J{\"o}rg and Polle, Andrea and Pollierer, Melanie M. and Scheu, Stefan and Schloter, Michael and Schulze, Ernst-Detlef and Schulze, Waltraud X. and Weinert, Jan and Weisser, Wolfgang W. and Wolters, Volkmar and Schrumpf, Marion},
  title     = {General relationships between abiotic soil properties and soil biota across spatial scales and different land-use types},
  series = {PLoS one},
  volume    = {7},
  journal   = {PLoS one},
  number    = {8},
  publisher = {PLoS},
  address   = {San Fransisco},
  issn      = {1932-6203},
  doi       = {10.1371/journal.pone.0043292},
  pages     = {8},
  year      = {2012},
  abstract  = {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.},
  language  = {en}
}