Metapopulation dynamics of a perennial plant, Succisa pratensis, in an agricultural landscape

Most metapopulation models neglect the local dynamics, and systems characterized by slow population turnover, time lags and non-equilibrium, are only rarely examined within a metapopulation context. In this study we used a realistic, spatially explicit, dynamic metapopulation model of a long-lived grassland plant, Succisa pratensis, to examine the relative importance of local population dynamics, and short and long-distance dispersal of seeds.     

Long-term trends and short-term variability of water quality in Skive Fjord, Denmark – nutrient load and mussels are the primary pressures and drivers that influence water quality

Nineteen years of monitoring data from the eutrophic Skive Fjord, Denmark were examined for linkages to external pressures and drivers, including nutrient inputs, meteorology and stocks of blue mussels. Linkages were examined by: 1) time-series analysis to document effects of nutrient reduction programs, 2) Pearson Rank correlations, 3) multivariate statistical analysis (PLS) to identify water quality variables with high predictability and their linkages to pressures, and 4) regression analysis to quantify relationships between pressures and water quality. Freshwater input, nitrogen load and phosphorus load showed decreasing trends through the period 1984–2002. The load reductions were only partially translated into trends in water quality: phosphorus decreased in most seasons, while total nitrogen decreased during winter and spring only. Phosphorus concentration had the highest predictability (explained by seasonal temperature variation) followed by transparency, silicate, tot-N, chlorophyll-a, primary productivity, phytoplankton diversity and phytoplankton turnover. The variation in pressures other than nutrient input confounded the relations between loads and water quality. High biomass of mussels led to reduced chlorophyll-a and increased transparency, while short-term variability in water column mixing led to changes in chlorophyll-a due to nutrient entrainment and coupling to benthic mussels.     

延长初发酵培养时间对大肠菌群结果影响分析

阐述了将《大肠菌群多管发酵法》(GB/T 4789.28-2003)初发酵时间24 h延长至48 h的原因,通过对139份不同种类样品进行大肠菌群总数检测,发现有12份样品增加了大肠菌群数,表明延长初发酵培养时间,可以增加迟缓发酵大肠菌群成员检出.指出迟缓发酵大肠菌群在44.5 ℃培养时,均无产酸产气现象,表明引起迟缓发酵的大肠菌群不属于粪大肠菌群成员.     

地震活动趋势的多层递阶预报

多层递阶预报是动态系统的新型统计预报理论,由于它把动态系统看成是一个时变参数系统,因而与客观实际较为符合,预报误差也相对较小,利用它来预报一个地区未来的地震活动趋势,具有一定的实用价值。     

中国历代灾害性海潮频率特征及时间序列的分形研究

根据中国历代灾害性海潮史料,运用一般统计方法和分形理论对其时间序列进行了分析,不仅探讨了历史灾害性海潮随时间演进的频率变化特征,而且判定了灾害性海潮时间序列的分形性质,在经基础上,还深入讨论了其分维与历代灾害性海潮暴积累发生次数和累积发生频率之间的关系,以及分维随时间演进的变化趋势。     

Model selection in demographic time series using VC-bounds

The problem of distinguishing density-independent (DI) from density-dependent (DD) demographic time series is important for understanding the mechanisms that regulate populations of animals and plants. We address this problem in a novel way by means of Statistical Learning Theory (SLT); SLT is built around the idea of VC-dimension, a complexity index for classes of parameterized functions. Though VC-dimensions of nonlinear models are generally unknown, in the linear case VC-dimension actually corresponds to the number of free parameters; this allows one to straightforwardly apply the model selection framework developed within SLT, and called Structural Risk Minimization (SRM). We generate noisy artificial time series, both DI and DD, and use SRM to recognize the model underlying the data, choosing among a suite of both density-dependent and independent demographies. We show that SRM significantly outperforms traditional model selection approaches, such as the Schwartz Information Criterion and Final Prediction Error in recognizing both density-dependence and independence.     

Lake management techniques in Florida,USA: Costs and water quality effects

Economic evaluations of restored or enhanced lakes in Florida indicated gravity drawdown was the least expensive action, whereas effluent diversion was 10,000 times more costly on a per hectare basis, with the other lake treatment costs occurring in the following order: gravity drawdown < grass carp introduction < mechanical drawdown < aeration < stormwater control = drawdown-dredging < effluent diversion. Within a particular treatment category, the costs spanned approximately one and one half orders of magnitude. Contrary to the abundant cost data, which permitted an economic analysis, inappropriate statistical design and lack of commitment toward sampling Florida's restored lakes undermines attempts to understand long-term water quality responses to various enhancement techniques. Using Lake Tohopekaliga as a case study, ordinary statistical tests produced contradictory and unreliable interpretations on the effectiveness of drawdown and phosphorus removal at sewage treatment plants in improving the trophic state index. This emphasizes the need for more robust statistical approaches and more detailed data collection in evaluating lake restoration activities It is unfortunate for Florida's lake restoration program that quantitative conclusions based on inferential statistics, replete with tests of assumptions, is limited to very few lakes     

Quantifying parameter uncertainty in a coral reef model using Metropolis-Coupled Markov Chain Monte Carlo

Coral reefs are threatened ecosystems, so it is important to have predictive models of their dynamics. Most current models of coral reefs fall into two categories. The first is simple heuristic models which provide an abstract understanding of the possible behaviour of reefs in general, but do not describe real reefs. The second is complex simulations whose parameters are obtained from a range of sources such as literature estimates. We cannot estimate the parameters of these models from a single data set, and we have little idea of the uncertainty in their predictions.We have developed a compromise between these two extremes, which is complex enough to describe real reef data, but simple enough that we can estimate parameters for a specific reef from a time series. In previous work, we fitted this model to a long-term data set from Heron Island, Australia, using maximum likelihood methods. To evaluate predictions from this model, we need estimates of the uncertainty in our parameters. Here, we obtain such estimates using Bayesian Metropolis-Coupled Markov Chain Monte Carlo. We do this for versions of the model in which corals are aggregated into a single state variable (the three-state model), and in which corals are separated into four state variables (the six-state model), in order to determine the appropriate level of aggregation. We also estimate the posterior distribution of predicted trajectories in each case.In both cases, the fitted trajectories were close to the observed data, but we had doubts about the biological plausibility of some parameter estimates. We suggest that informative prior distributions incorporating expert knowledge may resolve this problem. In the six-state model, the posterior distribution of state frequencies after 40 years contained two divergent community types, one dominated by free space and soft corals, and one dominated by acroporid, pocilloporid, and massive corals. The three-state model predicts only a single community type. We conclude that the three-state model hides too much biological heterogeneity, but we need more data if we are to obtain reliable predictions from the six-state model. It is likely that there will be similarly large, but currently unevaluated, uncertainty in the predictions of other coral reef models, many of which are much more complex and harder to fit to real data.     

挪威石油业安全员制度及其对我国同行业建立安全员制度的启示

研究挪威员工参与民主管理制度的理论框架,分析挪威石油行业安委会、安全员发展的背景及现状,探讨挪威法律中安全员及员工的职能与义务,同时了解挪威安全员工作存在的问题及改进的措施。结合我国石油天然气行业的安全生产现状,在不断完善HSE管理体系的基础上,借鉴挪威石油行业安全生产管理的成功经验,建议在我国石油天然气行业建立安全员制度并在立法中确立其职能和义务,进一步提高行业及企业的安全管理水平。     

Farmworker children's residential non-dietary exposure estimates from micro-level activity time series

Farmworkers' children may have increased pesticide exposure through dermal absorption and non-dietary ingestion, routes that are difficult to measure and model. The Cumulative Aggregate Simulation of Exposure (CASE) model, integrates the complexity of human behavior and variability of exposure processes by combining micro-level activity time series (MLATS) and mechanistic exposure equations. CASE was used to estimate residential non-dietary organophosphate pesticide exposure (i.e., inhalation, dermal, and non-dietary ingestion) to California farmworker children and evaluate the micro-activity approach. MLATS collected from children and distributions developed from pesticide measurements in farmworkers' residences served as inputs. While estimated diazinon exposure was greater for inhalation, chlorpyrifos exposure was greater for the other routes. Greater variability existed between children (σ_B² = 0.22–0.39) than within each child's simulations (σ_W² = 0.01–0.02) for dermal and non-dietary ingestion. Dermal exposure simulations were not significantly different than measured values from dosimeters worn by the children. Non-dietary ingestion exposure estimates were comparable to duplicate diet measurements, indicating this route may contribute substantially to aggregate exposure. The results suggest the importance of the micro-activity approach for estimating non-dietary exposure. Other methods may underestimate exposure via these routes. Model simulations can be used to identify at-risk children and target intervention strategies.