Development of paleolimnological inference models for pH, total nitrogen and specific conductivity based on planktonic diatoms

Canonical correspondence analysis (CCA was used to explore and identify statistically significant relationships between the distributions of planktonic diatoms and the physical and chemical properties of 50 Connecticut lakes. Six variables (pH, total nitrogen, calcium, sulfate, potassium and chlorophyll- a concentrations) were found to be significantly correlated with either or both of the first two extracted axes. The pH and calcium concentration, and to a lesser extent total nitrogen concentrations, were the most important variables controlling the distributions of planktonic diatoms in this suite of lakes. Paleolimnological inference models were developed for pH, total nitrogen (TN) and specific conductivity. Weighted averaging with (WAtol) and without (WA) tolerance downweighting, with and without bootstrap resampling techniques, and using either classical or inverse deshrinking methods were used to develop inference models for each variable. The pH and TN yielded sufficiently high 1/2 ratios and a highly significant first (constrained) axis when entered as single variables in both constrained and partially constrained CCA analyses, supporting the idea that reliable inference models could be developed for these variables. The r2 and RMSE of prediction values ranged from 0.73 to 0.86 and 0.37 to 0.6, respectively for pH, and from 0.4 to 0.64 and 59 g/l to 95 g/l, respectively for TN. Inference models for specific conductivity also yielded significant goodness-of-fit statistics. However, because specific conductivity was removed from the CCA analysis due to its high variance inflation factor and did not yield a significant relationship when entered as the sole variable in a partial constrained CCA, inference models for this variable will probably not yield any additional environmental information. The use of only planktonic diatoms in construction of inference models is discussed.     

由我国历史飞蝗北界记录得到的古气候推断

本文利用我国古代有关飞蝗的文献记录,整理出近1000年来飞蝗记录地域北界变动资料,根据飞蝗的生态习性,推断出飞蝗发生在我国北纬41°以北地区的年份的气温条件指出1162～1177年、1265～1280年和1763～1773年是我国东北地区气候温暖的时段,这分别为南宋气候、中世纪温暖期和小冰期的回暖期提出新的佐证。     

双黑洞系统的不同起源及其参数分布

双黑洞组成的近密双星系统并合是激光干涉仪引力波天文台等地基引力波探测器的主要探测对象。随着探测器灵敏度的提高,大量该类信号的探测将成为进一步研究黑洞物理的有效工具。但是目前对双黑洞系统的起源机制和内禀参数分布等物理问题的研究还不够深入,例如由引力波探测得到的黑洞质量分布与X射线双星观测的结果存在较大差异,还未有很好的理论模型可解释该结果。目前普遍认为双黑洞系统主要有两种起源:大质量双星演化机制和动力学起源机制。基于这两类起源的双黑洞系统在质量、自旋分布等方面存在差异。因此可在贝叶斯理论框架下,利用引力波信号携带的波源质量和自旋等信息,推断波源起源,计算不同起源的双黑洞系统所占比例,以及检验质量自旋等参数分布的差异。     

Systematic increase in model complexity helps to identify dominant streamflow mechanisms in two small forested basins

This study shows how the use of increasing model complexity allows us to hypothesize about dominant streamflow mechanisms in two small Brazilian forested basins. Nine different structures from SUPERFLEX, an objective framework to systematically increase hydrological model complexity, were tested and we extended the flexible modelling methodology to error models as well. We show that applying a rigorous methodology in a model evaluation framework, with residual analysis and control of model complexity, is essential for testing a model as a hypothesis for dominant hydrological controls. Our results indicate that the model architecture was more important than the increase in the number of model parameters. Better performing models were those with a parallel structure, which confirms our a priori belief about the dominant runoff mechanisms of the studied catchments, characterized by a rapid response to rainfall, but also a constant river discharge fed by water storage on the thick soil layer.     

青岛海雾的自适应神经模糊推理系统建模

基于自适应神经模糊推理系统(ANFIS),通过对历史观测资料的训练和规则提取,建立了青岛海雾(能见度)与该站及其上游测站的风向、风速、湿度等要素的逻辑映射关系和模糊推理模型,进而能够较为客观、定量地描述和诊断青岛海雾的发生发展,对改进和提高青岛海雾预报有一定的参考应用意义。     

Sources of bias in weighted average inferences of environmental conditions

Weighted averaging is widely used for inferring environmental conditions from an observed species assemblage. However, weighted average inferences are known to be systematically biased, and linear corrections (i.e., deshrinking functions) are commonly applied to adjust for this bias. In this analysis, the magnitude of the biases in weighted average inferences (and therefore the values of the deshrinking coefficients) are shown to depend upon the range of conditions sampled in the calibration data set and the true optima and niche breadths of the species observed in the calibration data set. Since the range of conditions and the observed species can differ between the calibration data set and the new data set for which environmental conditions are inferred, the coefficients for the deshrinking function derived using the calibration data may not be applicable to inferences computed using a new data set. Thus, environmental inferences may still exhibit systematic errors even after application of the linear correction. The findings from the theoretical analysis are demonstrated using stream temperature and macroinvertebrate data collected from wadeable streams in the western United States.     

The Uncertainty in the True End Point of a Fossil's Stratigraphic Range When Stratigraphic Sections Are Sampled Discretely

Stratigraphic sections are often sampled at well-defined discrete points. Because of the incompleteness of the fossil record, a particular species may not be observed even when it is extant at a sampling point. We introduce a model and Bayesian analysis for estimating the true time of disappearance of a lineage from a section in the face of the possibility that failure to find the species beyond its observed stratigraphic range may represent false negatives. We incorporate proper prior information, including an estimated longevity of the species and the probability that it will be observed if extant. Our analysis produces a posterior density for the true extinction time of the species. Summaries of this probability distribution provide a point estimate of the extinction time, a standard deviation for the uncertainty in the estimate, and confidence intervals for the time of extinction. We apply our model to stratigraphic ranges of benthic foraminifera collected from the early Late Cretaceous (Cenomanian and Turonian) from Eastbourne, England.