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The Olorgesailie Drilling Project and the related Hominin Sites and Paleolakes Drilling Project in East Africa were initiated to test hypotheses and models linking environmental change to hominin evolution by drilling lake basin sediments adjacent to important archeological and paleoanthropological sites. Drill core OL012-1A recovered 139 m of sedimentary and volcaniclastic strata from the Koora paleolake basin, southern Kenya Rift, providing the opportunity to compare paleoenvironmental influences over the past million years with the parallel record exposed at the nearby Olorgesailie archeological site. To refine our ability to link core-to-outcrop paleoenvironmental records, we institute here a methodological framework for deriving a robust age model for the complex lithostratigraphy of OL012-1A. Firstly, chronostratigraphic control points for the core were established based on 4 Ar/39Ar ages from intercalated tephra deposits and a basal trachyte flow, as well as the stratigraphic position of the Brunhes-Matuyama geomagnetic reversal. This dataset was combined with the position and duration of paleosols, and analyzed using a new Bayesian algorithm for high-resolution age-depth modeling of hiatus-bearing stratigraphic sections. This model addresses three important aspects relevant to highly dynamic, nonlinear depositional environments: 1) correcting for variable rates of deposition, 2) accommodating hiatuses, and 3) quantifying realistic age uncertainty with centimetric resolution. Our method is applicable to typical depositional systems in extensional rifts as well as to drill cores from other dynamic terrestrial or aquatic environments. We use the core age model and lithostratigraphy to examine the inter connectivity of the Koora Basin to adjacent areas and sources of volcanism. (C) 2019 Elsevier Ltd. All rights reserved.
Developments of future scenarios of Antarctic ecosystems are still in their infancy, whilst predictions of the physical environment are recognized as being of global relevance and corresponding models are under continuous development. However, in the context of environmental change simulations of the future of the Antarctic biosphere are increasingly demanded by decision makers and the public, and are of fundamental scientific interest. This paper briefly reviews existing predictive models applied to Antarctic ecosystems before providing a conceptual framework for the further development of spatially and temporally explicit ecosystem models. The concept suggests how to improve approaches to relating species' habitat description to the physical environment, for which a case study on sea urchins is presented. In addition, the concept integrates existing and new ideas to consider dynamic components, particularly information on the natural history of key species, from physiological experiments and biomolecular analyses. Thereby, we identify and critically discuss gaps in knowledge and methodological limitations. These refer to process understanding of biological complexity, the need for high spatial resolution oceanographic data from the entire water column, and the use of data from biomolecular analyses in support of such ecological approaches. Our goal is to motivate the research community to contribute data and knowledge to a holistic, Antarctic-specific, macroecological framework. Such a framework will facilitate the integration of theoretical and empirical work in Antarctica, improving our mechanistic understanding of this globally influential ecoregion, and supporting actions to secure this biodiversity hotspot and its ecosystem services.
Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making.
Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop-and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making.
The Westerlund 1 (Wd1) cluster hosts a rich and varied collection of massive stars. Its dynamical youth and the absence of ongoing star formation indicate a coeval population. As such, the simultaneous presence of both late-type supergiants and Wolf-Rayet stars has defied explanation in the context of single-star evolution. Observational evidence points to a high binary fraction, hence this stellar population offers a robust test for stellar models accounting for both single-star and binary evolution. We present an optical to near-IR (VLT & NTT) spectroscopic analysis of 22 WR stars in Wd 1, delivering physical properties for the WR stars.
We discuss how these differ from the Galactic field population, and how they may be reconciled with the predictions of single and binary evolutionary models.
Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making.
Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop-and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making.