The pelagic community of a gravel pit lake: Significance of Coleps hirtus viridis (Prostomatida) and its role as a scavenger

Coleps hirtus viridis was the dominant species of the planktonic ciliate community of Lake Fühlinger See (Germany) during the study in 1999 and 2000. Total ciliate densities ranged from 120 to 42,000 ind. l⁻¹ in 1999 and up to 8,000 ind. l⁻¹ in 2000. Coleps contributed up to 98% to both total ciliate abundance and biomass and made up an average of 64% of the total ciliate biomass. Oligotrichs (Rimostrombidium, Strobilidium) dominated the epilimnetic zone, whereas peritrich ciliates (Pelagovorticella, Vorticella) were predominantly located in the hypolimnion. The population maximum of Coleps changed locations from the epilimnion in early summer to the hypolimnion (up to 40,000 ind. l⁻¹) during stratification. High growth rates in the hypolimnion, presence of endosymbiontic algae and the ability to ingest detritus seem to be important for the success.Growth rates of Coleps in June were determined by Landry-Hassett dilution experiments in both the epilimnion and the hypolimnion. The instantaneous growth rates were similar in both layers (0.6 d⁻¹), but a distinctly higher instantaneous mortality was estimated for the epilimnion. These high loss rates may be due to grazing pressure by cladocerans.The significance of the histophagous feeding of Coleps was evaluated through an experiment using killed zooplankton. Parts of Daphnia magna were incorporated at rates of about 1,100 μm³ ind.⁻¹ h⁻¹ by Coleps without endosymbiotic algae and at rates of 500 μm³ ind.⁻¹ h⁻¹ by Coleps with endosymbionts. These high feeding rates support the conclusion that Coleps can use dead organic matter as an additional food source.     

发现过程模型法对大港陆地探区油气资源的评价

对现有油气资源基本评价方法的原理、优缺点、适应性做了分析总结，重点剖析了在油藏总体分布服从帕莱托分布基础上建立起来的发现过程模型法．同时根据已建立的油气藏总体预测模型，对符合发现过程模型的大港陆地古近系成藏体系进行了油气资源评价。     

On the long-term seismic hazard analysis in the Zhangjiakou–Penglai seismotectonic zone, China

The Zhangjiakou–Penglai seismotectonic zone (ZPSZ) lies in the northern part of North China and extends along the Zhangjiakou–Beijing–Tianjin–Bohai Bay–Penglai–Yellow Sea. It is about 900 km long and some 250 km wide in a northwest direction. The great Sanhe-Pinggu (M_S=8.0) earthquake occurred on September 1679 and the Tangshan (M_S=7.8) earthquake on July 1976 caused serious economic and life losses. According to some differences in crust structure and regional tectonic stress field, the ZPSZ is divided into western and eastern segment by the 117°E line for study on long-term seismic hazard analysis. An analysis of Gutenberg–Richter's empirical relation of earthquake-frequency and time process of historic and recent earthquakes along the eastern and western segments shows that the earthquake activity obeys a Poisson process, and these calculations indicate that the earthquake occurrence probability of M_S=6.0–6.9 is 0.77–0.83 in the eastern segment and the earthquake occurrence probability of M_S=7.0–7.9 is 0.78–0.80 in the western segment of the ZPSZ during a period from 2005 to 2015.     

Indicator Simulation Accounting for Multiple-Point Statistics

Geostatistical simulation aims at reproducing the variability of the real underlying phenomena. When nonlinear features or large-range connectivity is present, the traditional variogram-based simulation approaches do not provide good reproduction of those features. Connectivity of high and low values is often critical for grades in a mineral deposit. Multiple-point statistics can help to characterize these features. The use of multiple-point statistics in geostatistical simulation was proposed more than 10 years ago, on the basis of the use of training images to extract the statistics. This paper proposes the use of multiple-point statistics extracted from actual data. A method is developed to simulate continuous variables. The indicator kriging probabilities used in sequential indicator simulation are modified by probabilities extracted from multiple-point configurations. The correction is done under the assumption of conditional independence. The practical implementation of the method is illustrated with data from a porphyry copper mine.     

Transformation of Residuals to Avoid Artifacts in Geostatistical Modelling with a Trend

Trend modelling is an important part of natural resource characterization. A common approach to account for a variable with a trend is to decompose it into a relatively smoothly varying trend and a more variable residual component. Then, the residuals are stochastically modelled independent of the trend. This decomposition can result in values outside the plausible range of variability, such as grades below zero or ratios that exceed 1.0. We transform the residuals conditional to the trend component to explicitly remove these complex features prior to geostatistical modelling. Back transformation of the modelled residual values allows the complex relations to be reproduced. A petroleum-related application shows the robustness of the proposed transformation. Furthermore, a mining application shows that when this conditional transformation is applied to the original variable, instead of the residual, simulated values are assured to be nonnegative.     

富宁两次大暴雨的对比研究

对比云南富宁2001年“8·25”与2004年“5·15”两次大暴雨过程前后的雨量分布、灾情、大气环流背景,以及反映大气水汽、热力、动力条件的部分物理量场,分析它们在两次过程中的异同点,讨论各类条件对暴雨落区的指示性情况,并对“5·15”过程的雷达回波资料作简单的定量分析。     

Geostatistical simulation of random fields with bivariate isofactorial distributions by adding mosaic models

This work deals with the geostatistical simulation of a family of stationary random field models with bivariate isofactorial distributions. Such models are defined as the sum of independent random fields with mosaic-type bivariate distributions and infinitely divisible univariate distributions. For practical applications, dead leaf tessellations are used since they provide a wide range of models and allow conditioning the realizations to a set of data via an iterative procedure (simulated annealing). The model parameters can be determined by comparing the data variogram and madogram, and enable to control the spatial connectivity of the extreme values in the realizations. An illustration to a forest dataset is presented, for which a negative binomial model is used to characterize the distribution of coniferous trees over a wooded area.