67 research outputs found
Organism-sediment interactions govern post-hypoxia recovery of ecosystem functioning
Hypoxia represents one of the major causes of biodiversity and ecosystem functioning loss for coastal waters. Since eutrophication-induced hypoxic events are becoming increasingly frequent and intense, understanding the response of ecosystems to hypoxia is of primary importance to understand and predict the stability of ecosystem functioning. Such ecological stability may greatly depend on the recovery patterns of communities and the return time of the system properties associated to these patterns. Here, we have examined how the reassembly of a benthic community contributed to the recovery of ecosystem functioning following experimentally-induced hypoxia in a tidal flat. We demonstrate that organism-sediment interactions that depend on organism size and relate to mobility traits and sediment reworking capacities are generally more important than recovering species richness to set the return time of the measured sediment processes and properties. Specifically, increasing macrofauna bioturbation potential during community reassembly significantly contributed to the recovery of sediment processes and properties such as denitrification, bedload sediment transport, primary production and deep pore water ammonium concentration. Such bioturbation potential was due to the replacement of the small-sized organisms that recolonised at early stages by large-sized bioturbating organisms, which had a disproportionately stronger influence on sediment. This study suggests that the complete recovery of organism-sediment interactions is a necessary condition for ecosystem functioning recovery, and that such process requires long periods after disturbance due to the slow growth of juveniles into adult stages involved in these interactions. Consequently, repeated episodes of disturbance at intervals smaller than the time needed for the system to fully recover organism-sediment interactions may greatly impair the resilience of ecosystem functioning.
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Nonlinear Dynamic Analyses of Perris Dam Using Transition Probability to Model Interbedded Alluvial Strata
This case study presents an application of a conditional transition probability method for interpreting subsurface stratigraphy for the interbedded alluvium underlying Perris Dam, and evaluating the effects of stratigraphic uncertainty on nonlinear dynamic analysis (NDA) results for design earthquake loading. The challenges involved in synthesizing information from different sources (i.e., geologic conditions, site investigation tools, lab data, field classifications) into soil categories for interbedded alluvium were examined. The application of conditional transition probability methods for developing three-dimensional (3D) realizations of the upper Holocene and lower Pleistocene alluvial strata over a 305-m-wide interval along the dam alignment is described, including challenges with insufficient data and limitations involved with utilizing a stationary, geostatistical method for approximating nonstationary geologic conditions. Two-dimensional (2D) NDA models of Perris Dam were created by slicing the 3D transition probability realizations into five 2D cross sections. The constitutive models PM4Sand and PM4Silt were used to model the sand and clay soil categories in the alluvial strata, as well as the different zones in the embankment. The deformations and variability in deformations for each cross section were compared, and sensitivity studies were completed to examine the impact of several factors, including impacts of the small-strain shear modulus for the alluvium, mean lengths and sills for the alluvium categories, strengths for each alluvium soil category, and different ground motions. NDA cross sections of Perris Dam with uniformly (noncategorical) distributed properties were performed with and without additional deterministic embedded soil lenses, and the deformations were compared with transition probability models and deterministic models completed by others. The use of conditional transition probability models for NDAs of Perris Dam, along with implications and lessons for practice, are discussed
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Using Conditional Random Fields for a Spatially Variable Liquefiable Foundation Layer in Nonlinear Dynamic Analyses of Embankments
Two-dimensional nonlinear dynamic analyses (NDAs) are performed for a series of hypothetical embankment dams on a spatially variable liquefiable foundation layer to evaluate the utility of representing the foundation layer with random fields conditioned on different levels of site characterization information. A set of two-dimensional parent models (PMs), each representing a true foundation condition, were generated using unconditional random fields of equivalent clean sand, corrected standard penetration test (N1)60cs values. Different levels of site characterization were then represented by combining different numbers of local borings (i.e., columns of data from the PM) with the optional inclusion of constraints on the geostatistical properties that might come from sitewide explorations. NDAs were performed using the same input motions for the PM (which represents perfect knowledge of soil conditions), a set of realizations conditioned on the local borings alone, and a set of realizations conditioned on the local borings with sitewide statistics. Embankment deformations obtained for the conditional realizations are compared to those for the PM to evaluate the potential benefits of increasing levels of site characterization in terms of deformation prediction accuracy. Parametric analyses include varying the embankment size, scales of fluctuation in the foundation stratum, number of conditioning borings, and ground motions. The results of these comparisons illustrate that the beneficial effects of using conditional random fields were generally limited to cases with the horizontal scale of fluctuation approaching the scale of the embankment base width and to cases with a large number of borings (more than three borings per horizontal scale of fluctuation), which may not be practical in many situations. Additional potential benefits and limitations of using conditional random fields for representing spatial variable liquefiable foundation layers in embankment dam NDAs are discussed
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