Effects of surrounding land use and water depth on seagrass dynamics relative to a catastrophic algal bloom

Seagrasses are the foundation of many coastal ecosystems and are in global decline because of anthropogenic impacts. For the Indian River Lagoon (Florida, U.S.A.), we developed competing multistate statistical models to quantify how environmental factors (surrounding land use, water depth, and time [year]) influenced the variability of seagrass state dynamics from 2003 to 2014 while accounting for time‐specific detection probabilities that quantified our ability to determine seagrass state at particular locations and times. We classified seagrass states (presence or absence) at 764 points with geographic information system maps for years when seagrass maps were available and with aerial photographs when seagrass maps were not available. We used 4 categories (all conservation, mostly conservation, mostly urban, urban) to describe surrounding land use within sections of lagoonal waters, usually demarcated by land features that constricted these waters. The best models predicted that surrounding land use, depth, and year would affect transition and detection probabilities. Sections of the lagoon bordered by urban areas had the least stable seagrass beds and lowest detection probabilities, especially after a catastrophic seagrass die‐off linked to an algal bloom. Sections of the lagoon bordered by conservation lands had the most stable seagrass beds, which supports watershed conservation efforts. Our results show that a multistate approach can empirically estimate state‐transition probabilities as functions of environmental factors while accounting for state‐dependent differences in seagrass detection probabilities as part of the overall statistical inference procedure.     

Optimizing conservation in species-specific agricultural landscapes

Recovery of grassland birds in agricultural landscapes is a global imperative. Agricultural landscapes are complex, and the value of resource patches may vary substantially among species. The spatial extent at which landscape features affect populations (i.e., scale of effect) may also differ among species. There is a need for regional-scale conservation planning that considers landscape-scale and species-specific responses of grassland birds to environmental change. We developed a spatially explicit approach to optimizing grassland conservation in the context of species-specific landscapes and prioritization of species recovery and applied it to a conservation program in Kentucky (USA). We used a hierarchical distance-sampling model with an embedded scale of effect predictor to estimate the relationship between landscape structure and abundance of eastern meadowlarks (Sturnella magna), field sparrows (Spizella pusilla), and northern bobwhites (Colinus virginianus). We used a novel spatially explicit optimization procedure rooted in multi-attribute utility theory to design alternative conservation strategies (e.g., prioritize only northern bobwhite recovery or assign equal weight to each species’ recovery). Eastern meadowlarks and field sparrows were more likely to respond to landscape-scale resource patch adjacencies than landscape-scale patch densities. Northern bobwhite responded to both landscape-scale resource patch adjacencies and densities and responded strongly to increased grassland density. Effects of landscape features on local abundance decreased as distance increased and had negligible influence at 0.8 km for eastern meadowlarks (0.7–1.2 km 95% Bayesian credibility intervals [BCI]), 2.5 km for field sparrows (1.5–5.8 km 95% BCI), and 8.4 km for bobwhite (6.4–26 km 95% BCI). Northern bobwhites were predicted to benefit greatly from future grassland conservation regardless of conservation priorities, but eastern meadowlark and field sparrow were not. Our results suggest similar species can respond differently to broad-scale conservation practices because of species-specific, distance-dependent relationships with landscape structure. Our framework is quantitative, conceptually simple, customizable, and predictive and can be used to optimize conservation in heterogeneous ecosystems while considering landscape-scale processes and explicit prioritization of species recovery.     

Dynamics of a low‐density tiger population in Southeast Asia in the context of improved law enforcement

Recovering small populations of threatened species is an important global conservation strategy. Monitoring the anticipated recovery, however, often relies on uncertain abundance indices rather than on rigorous demographic estimates. To counter the severe threat from poaching of wild tigers (Panthera tigris), the Government of Thailand established an intensive patrolling system in 2005 to protect and recover its largest source population in Huai Kha Khaeng Wildlife Sanctuary. Concurrently, we assessed the dynamics of this tiger population over the next 8 years with rigorous photographic capture‐recapture methods. From 2006 to 2012, we sampled across 624–1026 km² with 137–200 camera traps. Cameras deployed for 21,359 trap days yielded photographic records of 90 distinct individuals. We used closed model Bayesian spatial capture‐recapture methods to estimate tiger abundances annually. Abundance estimates were integrated with likelihood‐based open model analyses to estimate rates of annual and overall rates of survival, recruitment, and changes in abundance. Estimates of demographic parameters fluctuated widely: annual density ranged from 1.25 to 2.01 tigers/100 km², abundance from 35 to 58 tigers, survival from 79.6% to 95.5%, and annual recruitment from 0 to 25 tigers. The number of distinct individuals photographed demonstrates the value of photographic capture–recapture methods for assessments of population dynamics in rare and elusive species that are identifiable from natural markings. Possibly because of poaching pressure, overall tiger densities at Huai Kha Khaeng were 82–90% lower than in ecologically comparable sites in India. However, intensified patrolling after 2006 appeared to reduce poaching and was correlated with marginal improvement in tiger survival and recruitment. Our results suggest that population recovery of low‐density tiger populations may be slower than anticipated by current global strategies aimed at doubling the number of wild tigers in a decade.     

Accident modelling and analysis in process industries

Accident modelling is a methodology used to relate the causes and effects of events that lead to accidents. This modelling effectively seeks to answer two main questions: (i) Why does an accident occur, and (ii) How does it occur. This paper presents a review of accident models that have been developed for the chemical process industry with in-depth analyses of a class of models known as dynamic sequential accident models (DSAMs). DSAMs are sequential models with a systematic procedure to utilise precursor data to estimate the posterior risk profile quantitatively. DSAM also offers updates on the failure probabilities of accident barriers and the prediction of future end states. Following a close scrutiny of these methodologies, several limitations are noted and discussed, and based on these insights, future work is suggested to enhance and improve this category of models further.     

Modelling fertility: an application of count regression models

Often the lifecycle data occur as count of the vital events and are recorded as integers. The purpose of this article is to model the fertility behavior based on religious, educational, economic, and occupational characteristics. The responses of classified groups according to these determinants are examined for significant influence on fertility using Poisson regression model (PRM) based on the National Family Health Survey -3 dataset. The observed and predicted probabilities under PRM indicate modal value of two children for the Poisson distribution modeled data. Presence of dominance of two child in the data motivates the authors to adopt multinomial regression model (MRM) in order to link fertility with various socioeconomic indicators responsible for fertility variation. Choice of the explanatory factors is limited to the availability of data. Trends and patterns of preference for birth counts suggest that religion, caste, wealth, female education, and occupation are the dominant factors shaping the observed birth process. Empirical analysis suggests that both the models used in the study perform similarly on the sample data. However, fitting of MRM by taking birth count of two as comparison category shows improved Akaike information criterion and consistent Akaike information criterion values. Current work contributes to the existing literature as it attempts to provide more insight into the determinants of Indian fertility using Poisson and MRM.     

环境模型在风险评估中的应用

环境模型方法是化学品风险评估中经常使用的方法。环境模型可提供化学品的浓度、归趋和主要迁移转化途径等信息,目前已被广泛应用于各类环境系统中。本文对该方法使用的一般步骤进行介绍,并对我国的环境模型工作提出了几点建议。     

Revealing photon transmission in an ultraviolet reactor: Advanced approaches for measuring fluence rate distribution in water for model validation

Fluence rate (FR) distribution (optical field) is of great significance in the optimal design of ultraviolet (UV) reactors for disinfection or oxidation processes in water treatment. Since the 1970s, various simulation models have been developed, which can be combined with computational fluidic dynamic software to calculate the fluence delivered in a UV reactor. These models strive for experimental validation and further improvement, which is a major challenge for UV technology in water treatment. Herein, a review of the simulation models of the FR distribution in a UV reactor and the applications of the current main experimental measurement approaches including conventional flat-type UV detector, spherical actinometer, and micro-fluorescent silica detector (MFSD), is presented. Moreover, FR distributions in a UV reactor are compared between various simulation models and MFSD measurements. In addition, the main influential factors on the FR distribution, including inner-wall reflection, refraction and shadowing effects of adjacent lamps, and turbidity effect are discussed, which is helpful for improving the accuracy of the simulation models and avoiding dark regions in the reactor design. This paper provides an overview on the simulation models and measurement approaches for the FR distribution, which is helpful for the model selection in fluence calculations and gives high confidence on the optimal design of UV reactors in regard to present methods.     

低分子量有机酸提取土壤中部分重金属的拟合模型研究

为研究不同浓度低分子量有机酸与提取的土壤重金属含量之间的关系,本文以采自安徽省安庆市某铜矿区及其周围的土壤为例,通过浸提法,研究了不同浓度苹果酸、甲酸对采样点土壤中重金属Cu、Mn、Zn、Pb提取能力的影响并进行模型拟合.结果表明:不同浓度苹果酸和甲酸对采样点土壤中重金属的提取量不同,两种有机酸对低pH、高重金属含量采样点及其周围土壤中的重金属提取量较大,两种低分子量有机酸对S6采样点的重金属Mn的提取量最大,分别为322.5 mg·kg~(-1)和193.58 mg·kg~(-1),提取率达70.02%和42.03%,Zn、Pb次之,Cu最低;随着苹果酸、甲酸浓度的升高,土壤中重金属Cu、Mn、Zn、Pb的提取量增加,苹果酸对土壤中重金属的提取能力大于甲酸;拟合模型对于两种低分子量有机酸与其提取的土壤中部分重金属含量之间的关系,具有较好的拟合性,其决定系数R~2在0.8293～0.9990之间,都具有显著性(p0.05),该模型可以定量表征低分子量有机酸对土壤重金属的提取能力,此研究结果为土壤中重金属的固定化和增强植物修复受重金属污染的土壤提供了理论支持.     

液氨储罐泄漏扩散模型的改进研究

针对液氨储罐孔洞泄漏的实际工况,综合考虑泄漏及由于泄漏导致液氨闪蒸造成 的罐压变化,以及储罐的许用压力,对其连续泄漏过程进行了分析,以此改进现有的泄漏扩 散后果分析模型,获得较忽略这些因素更为严重的后果.最后针对灾难性事故发生前连续泄 漏最大持续时间的影响因素进行了分析.结果表明,环境温度与初始罐压对其影响较大,是决定...     

数值模拟技术在二沉池设计中的应用

二沉池的运行状况对整个污水处理系统的处理效果有重要影响。沉淀池的处理效果与沉淀池内水流特性密切相关。传统的沉淀池设计基于理想沉淀池假设和静置沉淀实验,对沉淀池内的水流特性考虑不多,设计参数选择范围宽,经验性强,不能保证沉淀池内的泥水分离和浓缩效果。计算流体力学是一种较为成熟的数值模拟技术,已广泛应用于水处理构筑物在设计条件下的内部水流特性和行为的研究,为预测沉淀池的运行状况和结构改进的效果提供了有效手段。本文对沉淀池内的水流特性和流体力学在沉淀池研究中的应用进行了描述。