Assessing interactions of multiple stressors when data are limited: A Bayesian belief network applied to coral reefs

Bayesian belief networks are finding increasing application in adaptive ecosystem management where data are limited and uncertainty is high. The combined effect of multiple stressors is one area where considerable uncertainty exists. Our study area, the Great Barrier Reef is simultaneously data-rich – concerning the physical and biological environment – and data-poor – concerning the effects of interacting stressors. We used a formal expert-elicitation process to obtain estimates of outcomes associated with a variety of scenarios that combined stressors both within and outside the control of local managers. There was much stronger consensus about certain stressor effects – such as between temperature anomalies and bleaching – than others, such as the relationship between water quality and coral cover. In general, the expert outlook for the Great Barrier Reef is pessimistic, with the potential for climate change effects potentially to overshadow the effects of local management actions.     

Setting conservation priorities in Fiji: Decision science versus additive scoring systems

There is a well-established scientific field – decision science – that can be used to rigorously set conservation priorities. Despite well-documented shortcomings, additive scoring approaches to conservation prioritization are still prevalent. This paper discusses the shortcomings and advantages of both approaches applied in Fiji to identify priorities for terrestrial protected areas. The two main shortcomings of using a scoring approach (discussed in Keppel (2014) [1]) that are resolved with decision science approaches (presented in Klein et al. (2014) [2]) in Fiji were (1) priorities did not achieve one of the most important stated conservation goals of representing ~40% of Fiji׳s major vegetation types and (2) the weighting of different selection criteria used was arbitrary. Both approaches considered expert knowledge and land–sea connections important to decision makers in Fiji, but only decision science can logically integrate both, in addition to other important considerations. Thus, decision makers are urged to use decision science and avoid additive scoring systems when prioritizing places for conservation. Fiji has the opportunity to be a global leader in using decision science to support integrated land–sea planning decisions.     

Identification and prioritization of uncertainties for management of Eastern Atlantic bluefin tuna (Thunnus thynnus)

In recent decades there has been steady progress towards a risk-based management approach for fisheries. An important first step in a risk analysis framework is scoping to identify, describe and catalog the sources of uncertainty that might have an impact on a fishery. This paper introduces a methodology based on a range of tools to formalize the process of elicitation of uncertainties, from both experts and stakeholders, for the International Commission for the Conservation of Atlantic Tunas (ICCAT). ICCAT is a regional fisheries management organization responsible for the conservation of tunas and other highly migratory fish in the Atlantic Ocean and its adjacent seas. The aim of the elicitation was to identify and prioritize uncertainties for inclusion in Operating Models for Management Strategy Evaluation (MSE). The tool presented in this paper supports the qualitative prioritization of uncertainties, while also visualizing the degree of consensus among stakeholders on particular issues. Perceptions of uncertainty in fisheries often vary widely among scientists, industry and other interest groups, so tools that can facilitate inclusion and representation of different opinions are useful where decision-making depends on broad agreement and more generally, where effective management depends on commitment from stakeholders.     

Automated high resolution mapping of coffee in Rwanda using an expert Bayesian network

African highland agro-ecosystems are dominated by small-scale agricultural fields that often contain a mix of annual and perennial crops. This makes such systems difficult to map by remote sensing. We developed an expert Bayesian network model to extract the small-scale coffee fields of Rwanda from very high resolution data. The model was subsequently applied to aerial orthophotos covering more than 99% of Rwanda and on one QuickBird image for the remaining part. The method consists of a stepwise adjustment of pixel probabilities, which incorporates expert knowledge on size of coffee trees and fields, and on their location. The initial naive Bayesian network, which is a spectral-based classification, yielded a coffee map with an overall accuracy of around 50%. This confirms that standard spectral variables alone cannot accurately identify coffee fields from high resolution images. The combination of spectral and ancillary data (DEM and a forest map) allowed mapping of coffee fields and associated uncertainties with an overall accuracy of 87%. Aggregated to district units, the mapped coffee areas demonstrated a high correlation with the coffee areas reported in the detailed national coffee census of 2009 (R² = 0.92). Unlike the census data our map provides high spatial resolution of coffee area patterns of Rwanda. The proposed method has potential for mapping other perennial small scale cropping systems in the East African Highlands and elsewhere.     

基于GIS的土石坝安全评价系统设计与实现

基于GIS的土石坝安全评价专家会商系统的研制与开发的关键技术,探讨开发过程中存在的一系列技术要点.系统以其及时的信息供给能力,较好的环境问题适应能力,方便的人机交互能力,能有效支持各层次决策.系统采用B/S体系结构,通过Web浏览器作为用户界面,Web服务器存贮和处理信息.会商系统大幅度提高土石坝安全管理的信息化水平,...     

基于面向对象的瓦斯安全预警专家系统研究

为了预防和减少煤矿瓦斯爆炸事故,首先对基于面向对象的煤矿瓦斯安全预警专家系统进行总体设计,其次对系统功能进行了开发实现,包括建立知识库和综合数据库,通过综合推理使得系统中的信息能够更加准确地表达出来,最后对系统进行了仿真实验,结果表明系统的预测结果与煤矿安全专家提供的结果基本一致,达到了系统设计的要求,可以在煤矿中推广使用。     

江西省区域性洪涝灾害模糊综合评判方法的研究

本文以江西省六个重点产粮县为代表点，选择湿润指数、有效灌溉面积、绿度值变化、气象产量及水域面积变化等综合因子，以双季早稻为主要研究对象，采用模糊综合评判方法进行洪涝灾害程度的评估，并提出了一种灾害损失的估算方法。     

对流天气临近预报技术的发展与研究进展

目前,临近预报技术主要包括雷暴识别追踪和外推预报技术、数值预报技术以及以分析观测资料为主的概念模型预报技术等。其中,识别追踪和外推预报技术主要以雷达资料为基础,在这方面,交叉相关外推和回波特征追踪识别外推是比较成熟的技术,已经用于许多的临近预报业务系统中,其缺陷是预报时效较短,准确率也不是很高。随着精细数值天气预报技术和计算机技术的发展,利用多普勒天气雷达资料和其它中小尺度观测资料进行数值模式初始化,来预报雷暴的发生、发展和消亡已经成为一个研究的热点,该技术发展很快但还不成熟。概念模型预报技术主要是通过综合分析多种中小尺度观测资料,包括雷达和气象卫星资料等,在此基础上建立雷暴发生、发展和消亡的概念模型,特别是边界层辐合线和强对流的密切关系等,再结合数值模式分析预报和其它外推技术的结果,然后建立雷暴临近预报的专家系统,其不但可以获取雷暴和对流降水移动、发展的信息,还可以预报它们的生成和消亡。检验和定性评估也表明,将多种资料和技术集于一体的概念模型专家系统,其临近预报的准确率最高,时效也最长,是临近预报技术未来发展的主要趋势之一。NCAR的Auto Nowcaster系统是雷暴临近预报概念模型专家系统的一个典型代表。     

CHARACTERIZATION OF FOODS BY INEXACT RULES:THE SEXIA EXPERT SYSTEM

SEXIA is an expert system that uses a new methodological approach to identify foods,particularly oliveoils according to varieties,olive zones and denominations of origin.The methodological approachprovides identification tools,associating a confidence degree or a belief interval to the final hypotheses.The certainty factor and the Dempster-Shaffer theory,with some modifications,have been implementedin SEXIA.The computer can work with 50 chemical parameters whose data have previously beenacquired by the food analyst via a dialogue in the Spanish language.The system has been verified with144 olive oil samples.In this paper some results obtained for distinguishing the Arbequina variety fromother varieties using SEXIA and the BMDP stepwise discriminant analysis program are presented.Finally,promising directions for future research are suggested.