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.     

Hierarchical Bayesian Spatial Models for Multispecies Conservation Planning and Monitoring

Abstract: Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data's spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church's sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence–absence data derived from regional monitoring programs to develop models with both landscape and site‐level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatial models outperformed analogous models developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatial models built from presence–absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad‐scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km² hexagons), can increase the relevance of habitat models to multispecies conservation planning.     

水文频率线型选择与综合过程中贝叶斯因子求解新方法

为克服水文频率线型选择和综合过程中,贝叶斯因子求解时参数先验分布确定和线型边缘分布数值积分这两个难点问题,联合应用了贝叶斯采样方法和最大熵原理(POME)求解参数后验分布表达式,然后应用参数采样结果中逐个样本近似求和方法代替线型边缘分布积分过程,进而建立了贝叶斯因子求解新方法。实例分析和Monte-Carlo统计试验验证了该方法的准确性和有效性。分析结果显示:序列长度和参数取值大小等因素对贝叶斯因子和水文线型后验概率求解结果影响较大;由于BIC准则的实质是通过寻求一组最优参数值进行贝叶斯因子求解,因此在受到不利因素影响时参数估计结果往往存在较大误差,使得BIC准则的分析结果也存在较大偏差。贝叶斯因子求解新方法能够克服上述不利因素的影响,可以合理地分析和描述参数不确定性,使得分析计算结果明显改善,因此所提新方法具有更好的实用性和可靠性。     

An integrated approach for predicting fates of reintroductions with demographic data from multiple populations

We devised a novel approach to model reintroduced populations whereby demographic data collected from multiple sites are integrated into a Bayesian hierarchical model. Integrating data from multiple reintroductions allows more precise population-growth projections to be made, especially for populations for which data are sparse, and allows projections that account for random site-to-site variation to be made before new reintroductions are attempted. We used data from reintroductions of the North Island Robin (Petroica longipes), an endemic New Zealand passerine, to 10 sites where non-native mammalian predators are controlled. A comparison of candidate models that we based on deviance information criterion showed that rat-tracking rate (an index of rat density) was a useful predictor of robin fecundity and adult female survival, that landscape connectivity and a binary measure of whether sites were on a peninsula were useful predictors of apparent juvenile survival (probably due to differential dispersal away from reintroduction sites), and that there was unexplained random variation among sites in all demographic rates. We used the two best supported models to estimate the finite rate of increase (λ) for populations at each of the 10 sites, and for a proposed reintroduction site, under different levels of rat control. Only three of the reintroduction sites had λ distributions completely >1 for either model. At two sites, λ was expected to be >1 if rat-tracking rates were <5%. At the other five reintroduction sites, λ was predicted to be close to 1, and it was unclear whether growth was expected. Predictions of λ for the proposed reintroduction site were less precise than for other sites because distributions incorporated the full range of site-to-site random variation in vital rates. Our methods can be applied to any species for which postrelease data on demographic rates are available and potentially can be extended to model multiple species simultaneously.     

基于BN-CREAM的深水井控压井作业风险诱因研究

深水井控压井作业是有效控制溢流演化为井喷事故的二级井控工艺屏障。为提高深水井控压井作业可靠性,采用 BN-CREAM方法对其风险诱因进行研究。结合深水井控压井作业特点,考虑共因失效等因素,采用贝叶斯网络方法建立深 水井控压井作业风险演化模型。应用人因可靠性分析CREAM法计算深水压井人因失误先验概率,参考海洋可靠性数据手 册OREDA确定深水井控设备失效先验概率。依托贝叶斯网络的逆向推理能力辨识压井作业的主要风险节点,从而实现对 深水井控压井作业风险诱因的有效预测和评估。研究表明:深水井控压井作业共包含6个关键风险根节点,且压井作业人 因可靠性要低于设备可靠性;3级子节点“压井方法选择不合理”对深水压井作业的成功起到至关重要的作用,需进一 步开展风险分析研究。     

基于贝叶斯估计的炼化装置动态告警管理方法研究

针对现有告警管理方法无法判断炼化装置运行中的过渡过程,对过渡过程不能进行准确有效地监测和管理,提出 了基于贝叶斯估计的动态告警线计算方法。通过训练历史过渡过程数据得到先验概率,自适应判断过渡过程和估计动态 告警线,解决了传统告警线的斜率不能赋值且只能依靠人为调节的问题,并可通过判断系统状态调节告警管理方式。经 常压塔原油进料流量调整和减压炉干气流量工艺调节的过渡过程现场数据验证,结果表明:与传统告警管理方法相比, 基于贝叶斯估计的动态告警管理方法的误告警总数量减少了87.34%,避免了告警洪水的发生,提高了炼化工艺运行的安 全性和可靠性。     

基于改进贝叶斯网络的加油站火灾爆炸分析

为了找出导致加油站发生火灾爆炸事故的基本事件及其可能性大小,以加油站火灾爆炸故障树为基础建立相应的贝叶斯网络风险模型。在FTA向BN转化算法的基础上对条件概率做出了修正。利用GeNIe软件计算加油站火灾爆炸事故基事件的后验概率,同时进行灵敏度和影响力分析。最后通过实例分析,找出了导致某加油站发生火灾爆炸事故危险性最大的因素集为:加油站接打手机、机械碰撞、给塑料容器加油、加油冒油、油枪渗漏等。结果表明,注重基事件的多态性和事件间逻辑关系合理性的新模型,能推算出更准确的基事件概率分布,同时可以找出导致事故发生的最有可能途径,为加油站事故预防,系统改进提供较为合理性建议。     

水平定向钻穿越施工的失效可能性研究

水平定向钻穿越施工是管道穿越工程的一种主要施工形式,在施工中由于地质条件、设备使用和穿越工艺选择等原因使其存在风险,因此有必要对其失效可能性进行研究。建立了包含“人、机、料、法、环”五方面的水平定向钻施工失效可能性指标评价体系,对于特征影响因素,如扩径比、管土摩擦系数等,运用回拖过程的力学分析得出其分布规律,用模糊化的方法将底层指标分值转化为先验概率,再代入贝叶斯网络进行概率推理,最终确定其失效可能性。以某管道工程采取水平定向钻穿越河流为例,得出其失效可能性等级为“较高”,最后提出了相应的控制措施。运用贝叶斯网络研究水平定向钻穿越施工的失效可能性具有重要的工程应用价值和现实意义。     

基于贝叶斯网络的化工园区地震次生灾害情景分析

为了研究化工园区内发生地震灾害后的事故演化过程,利用事故链模型对地震次生灾害演化过程进行分析,并将其转化为贝叶斯网络,确定各节点的变量与状态取值范围;通过相关文献及专家经验判断分析,获得贝叶斯网络中各节点的条件概率;依据贝叶斯网络的推理策略,比较不同地震烈度下,各化工事故发生的后验概率值,并探讨应急救援的及时性对地震次生灾害发生概率的影响,从关键要素遏制化工事故的发生,采取针对性的应对措施,尽可能的降低化工事故,有效的降低人员伤亡及财产损失。     

金属埋地管道腐蚀泄漏及大气扩散过程推理模型研究

为了定量评估埋地金属管道腐蚀泄漏的风险,将贝叶斯网络和蒙特卡罗模拟方法用于计算管道腐蚀泄漏及扩散范围概率。建立了埋地金属管道腐蚀泄漏贝叶斯网络,量化了各事件之间的关联。在已知先验概率的基础上,运用贝叶斯概率计算得到管道腐蚀泄漏的后验概率值。利用蒙特卡罗模拟方法对泄漏后气体扩散范围进行了数值模拟,给出了主要模型变量的概率密度函数,得到了扩散区域范围概率分布特征。研究结果表明,管道腐蚀泄漏过程受输送介质和土壤腐蚀性、防腐措施有效性和大气扩散条件的影响,具有较大的不确定性。分析方法考虑了模型参数随机性对计算结果的影响,评估结果可以用于比较不同条件下埋地金属管道腐蚀泄漏扩散的风险。