The Importance of Being Spatial (and Reserved): Assessing Northern Spotted Owl Habitat Relationships with Hierarchical Bayesian Models

Abstract:  Regional conservation planning increasingly draws on habitat suitability models to support decisions regarding land allocation and management. Nevertheless, statistical techniques commonly used for developing such models may give misleading results because they fail to account for 3 factors common in data sets of species distribution: spatial autocorrelation, the large number of sites where the species is absent (zero inflation), and uneven survey effort. We used spatial autoregressive models fit with Bayesian Markov Chain Monte Carlo techniques to assess the relationship between older coniferous forest and the abundance of Northern Spotted Owl nest and activity sites throughout the species' range. The spatial random-effect term incorporated in the autoregressive models successfully accounted for zero inflation and reduced the effect of survey bias on estimates of species–habitat associations. Our results support the hypothesis that the relationship between owl distribution and older forest varies with latitude. A quadratic relationship between owl abundance and older forest was evident in the southern portion of the range, and a pseudothreshold relationship was evident in the northern portion of the range. Our results suggest that proposed changes to the network of owl habitat reserves would reduce the proportion of the population protected by up to one-third, and that proposed guidelines for forest management within reserves underestimate the proportion of older forest associated with maximum owl abundance and inappropriately generalize threshold relationships among subregions. Bayesian spatial models can greatly enhance the utility of habitat analysis for conservation planning because they add the statistical flexibility necessary for analyzing regional survey data while retaining the interpretability of simpler models.     

Estimating freshwater turtle mortality rates and population declines following hook ingestion

Freshwater turtle populations are susceptible to declines following small increases in the mortality of adults, making it essential to identify and understand potential threats. Freshwater turtles ingest fish hooks associated with recreational angling, and this is likely a problem because hook ingestion is a source of additive mortality for sea turtles. We used a Bayesian‐modeling framework, observed rates of hook ingestion by freshwater turtles, and mortality of sea turtles from hook ingestion to examine the probability that a freshwater turtle in a given population ingests a hook and subsequently dies from it. We used the results of these analyses and previously published life‐history data to simulate the effects of hook ingestion on population growth for 3 species of freshwater turtle. In our simulation, the probability that an individual turtle ingests a hook and dies as a result was 1.2–11%. Our simulation results suggest that this rate of mortality from hook ingestion is sufficient to cause population declines. We believe we have identified fish‐hook ingestion as a serious yet generally overlooked threat to the viability of freshwater turtle populations.     

避雷针(网)失效分析

按照应力、疲劳、锈蚀、腐蚀等失效原理,结合由于雷击所造成的具体事故案例,对避雷针（网）进行失效分析。同时,从“人机环”本质安全化的角度,提出了预防、预控、预测的对策。     

A Comparison of Bayesian Methods for Uncertainty Analysis in Hydraulic and Hydrodynamic Modeling

We evaluate and compare the performance of Bayesian Monte Carlo (BMC), Markov chain Monte Carlo (MCMC), and the Generalized Likelihood Uncertainty Estimation (GLUE) for uncertainty analysis in hydraulic and hydrodynamic modeling (HHM) studies. The methods are evaluated in a synthetic 1D wave routing exercise based on the diffusion wave model, and in a multidimensional hydrodynamic study based on the Environmental Fluid Dynamics Code to simulate estuarine circulation processes in Weeks Bay, Alabama. Results show that BMC and MCMC provide similar estimates of uncertainty. The posterior parameter densities computed by both methods are highly consistent, as well as the calibrated parameter estimates and uncertainty bounds. Although some studies suggest that MCMC is more efficient than BMC, our results did not show a clear difference between the performance of the two methods. This seems to be due to the low number of model parameters typically involved in HHM studies, and the use of the same likelihood function. In fact, for these studies, the implementation of BMC results simpler and provides similar results to MCMC. The results of GLUE are, on the other hand, less consistent to the results of BMC and MCMC in both applications. The posterior probability densities tend to be flat and similar to the uniform priors, which can result in calibrated parameter estimates centered in the parametric space.     

Dynamic failure assessment of an ammonia storage unit: A case study

Chemical Process Industries usually contain a diverse inventory of hazardous chemicals and complex systems required to perform process operations such as storage, separation, reaction, compression etc. The complex interactions between the equipment make them vulnerable to catastrophic accidents. Risk and failure assessment provide engineers with an intuitive tool for decision making in the operation of such plants. Abnormal events and near-miss situations occur regularly during the operation of a system. Accident Sequence Precursors (ASP) can be used to demonstrate the real-time operating condition of a plant. Dynamic Failure Assessment (DFA) methodology is based on Bayesian statistical methods incorporates ASP data to revise the generic failure probabilities of the systems during its operational lifetime.In this paper, DFA methodology is applied on an ammonia storage unit in a specialized chemical industry. Ammonia is stored in cold storage tanks as liquefied gas at atmospheric pressure. These tanks are susceptible to failures due to various abnormal conditions arising due process failures.Tank failures due to three such abnormal conditions are considered. Variation of the failure probability of the safety systems is demonstrated. The authors use ASP data collected from plant specific sources and safety expert judgement. The failure probabilities of some safety systems concerned show considerable deviation from the generic values. The method helps to locate the components which have undergone more degradation over the period and hence must be paid attention to. In addition, a Bayesian predictive model has been used to predict the number of abnormal events in the next time interval. The user-friendly and intuitive nature of the tool makes it appropriate for application in safety assessment reports in process industries.     

Operational risk assessment: A case of the Bhopal disaster

Accidental releases of hazardous chemicals from process facilities can cause catastrophic consequences. The Bhopal disaster resulting from a combination of inherently unsafe designs and poorly managed operations is a well-known case. Effective risk modeling approaches that provide early warnings are helpful to prevent and control such rare but catastrophic events. Probability estimation of these events is a constant challenge due to the scarcity of directly relevant data. Therefore, precursor-based methods that adopt the Bayesian theorem to update prior judgments on event probabilities using empirical data have been proposed. The updated probabilities are then integrated with consequences of varying severity to produce the risk profile.This paper proposes an operational risk assessment framework, in which a precursor-based Bayesian network approach is used for probability estimation, and loss functions are applied for consequence assessment. The estimated risk profile can be updated continuously given real-time operational data. As process facilities operate, this method integrates a failure-updating mechanism with potential consequences to generate a real-time operational risk profile. The real time risk profile is valuable in activating accident prevention and control strategies. The approach is applied to the Bhopal accident to demonstrate its applicability and effectiveness.     

The theoretical value of encounters with parasitized hosts for parasitoids

A female parasitoid searching for hosts in a patch experiences a diminishing encounter rate with unparasitized and thus suitable hosts. To use the available time most efficiently, it constantly has to decide whether to stay in the patch and continue to search for hosts or to search for and travel to another patch in the habitat. Several informational cues can be used to optimize the searching success. Theoretically, encounters with unparasitized hosts should lead to a prolonged search in a given patch if hosts are distributed contagiously. The results of empirical studies strongly support this hypothesis. However, it has, to date, not been investigated theoretically whether encounters with already parasitized hosts (which usually entail time costs) provide a parasitoid with valuable information for the optimization of its search in depletable patches, although the empirical studies concerning this question so far have produced ambiguous results. Building on recent advances in Bayesian foraging strategies, we approached this problem by modeling a priori searching strategies (which differ in the amount of information considered) and then testing them in computer simulations. By comparing the strategies, we were able to determine whether and how encounters with already parasitized hosts can yield information that can be used to enhance a parasitoid’s searching success.

The effect of afforestation on mineralization of soil organic matter

The effect of afforestation on the activity of microbiological mineralization of soil organic matter has been studied in Siberia. The results show that this effect concerns mainly net nitrogen mineralization and net nitrification, while carbon mineralization (CO₂ formation) does not depend on the type of ecosystem. It is proposed to use the rates of net nitrogen mineralization and nitrification as the most sensitive indicators of changes in an ecosystem.     

基于神经网络的泥石流预测

作者用神经网络方法对泥石流预测进行了探讨，讨论了训练参数的选择及训练方法，并结合泥石流预报的具体情况，对ＢＰ模型进行了适当的改进。研究表明，模型及算法是可行的。