Monitoring,imperfect detection,and risk optimization of a Tasmanian devil insurance population

Most species are imperfectly detected during biological surveys, which creates uncertainty around their abundance or presence at a given location. Decision makers managing threatened or pest species are regularly faced with this uncertainty. Wildlife diseases can drive species to extinction; thus, managing species with disease is an important part of conservation. Devil facial tumor disease (DFTD) is one such disease that led to the listing of the Tasmanian devil (Sarcophilus harrisii) as endangered. Managers aim to maintain devils in the wild by establishing disease‐free insurance populations at isolated sites. Often a resident DFTD‐affected population must first be removed. In a successful collaboration between decision scientists and wildlife managers, we used an accessible population model to inform monitoring decisions and facilitate the establishment of an insurance population of devils on Forestier Peninsula. We used a Bayesian catch‐effort model to estimate population size of a diseased population from removal and camera trap data. We also analyzed the costs and benefits of declaring the area disease‐free prior to reintroduction and establishment of a healthy insurance population. After the monitoring session in May–June 2015, the probability that all devils had been successfully removed was close to 1, even when we accounted for a possible introduction of a devil to the site. Given this high probability and the baseline cost of declaring population absence prematurely, we found it was not cost‐effective to carry out any additional monitoring before introducing the insurance population. Considering these results within the broader context of Tasmanian devil management, managers ultimately decided to implement an additional monitoring session before the introduction. This was a conservative decision that accounted for uncertainty in model estimates and for the broader nonmonetary costs of mistakenly declaring the area disease‐free.     

环境监测点位“组合校验码”编码模型及编码管理系统设计

分析了环境监测点位编码管理中存在的问题,提出科学合理的编码应该是具有科学性、唯一性、稳定性、简明性、规范性、可扩展性等特点。设计了一种简单"组合校验码"模型来适应不同环境要素类型的环境监测工作需要。同时,设计了一套覆盖各层级环境监测机构,各种环境要素类型,适应市场化监测服务,满足不同点位管控类别的编码管理系统来支撑"组合校验码"模型的使用,通过该系统可以有效解决环境监测数据集成程度低、共享困难等问题。     

河流水质自动监测站选址适宜性评价

为建立和量化适于河流水质自动监测站选址的评价方法体系,提出了河流水质自动监测站选址一致性、可行性和适宜性评价问题,并建立了选址可行性和适宜性评价2套指标体系。以自动站选址与手工断面位置不一致为前提,利用累乘指数判断选址可行性,在可行的基础上利用模糊综合评价进一步分析选址的适宜性。应用案例显示,罗汉大桥断面水质自动站选址于手工断面下游150 m处是可行的,并且此选址高度适宜,在具有水质代表性的同时兼顾成本和运维管理需求。     

氨气敏电极法和纳氏试剂分光光度法对地表水中氨氮的比对测试

采用不同质量浓度的氨氮标准样品和实际样品,用氨气敏电极法和纳氏试剂分光光度法进行同步测试。结果表明,2种分析方法在水样氨氮质量浓度在0. 159～2. 81 mg/L范围内具有良好的可比性、精密性和准确性。氨气敏电极法的检出限为0. 03 mg/L,平行6次测定样品的相对标准偏差为0. 4%～4. 2%,加标回收率为85. 0%～110%;纳氏试剂分光光度法的检出限为0. 025 mg/L,平行6次测定样品的相对标准偏差为0. 5%～6. 4%,加标回收率为93. 0%～99. 8%。同时氨气敏电极法在样品预处理、试剂配制和分析时间上要优于纳氏试剂分光光度法。氨气敏电极法能够满足地表水自动监测在线比对实际工作的需求,该方法具有良好的适用性。     

广东省“十二五”生态环境时空变化趋势及影响因素

基于2011—2015年Landsat7、Landsat8等卫星遥感影像,结合土壤侵蚀面积、水资源量、降雨量、污染物排放量等统计数据,按照《生态环境状况评价技术规范》(HJ 192—2015),研究评价广东省"十二五"期间生态环境状况及其时空变化趋势,并对其影响因素进行综合分析。结果表明,广东省的生态环境状况总体为优,各市生态环境状况均属优、良,粤北生态环境状况整体最好;广东省及各市生态环境状况稳中趋好,但呈现温和地波动变化;主要污染物排放量下降和水资源总量提高是促进生态环境状况改善的主要原因。     

地表水环境遥感监测关键技术与系统

介绍了地表水环境遥感监测的关键技术与系统及其典型应用,其代表性机理模型和应用示范成果主要来自于中国科学院遥感与数字地球研究所的高光谱遥感团队在最近几年中取得的一些研究进展,主要包括建立了基于改进双峰法的水体分布自动化遥感提取方法,实现了简单、高效和高精度的水体提取；提出了大型湖泊长时序水量估算方法,并以青藏高原湖区为例,重建了典型湖泊面积、水位和水量序列；发展了基于“软分类”的典型内陆水体叶绿素a浓度反演方法,构建了基于生物光学模型的高度浑浊水体悬浮物浓度遥感反演半解析方法,提高了反演方法的区域和季节适用性；构建了基于水色指数的大范围湖库营养状态和透明度遥感监测方法,实现了全球大型湖库营养状态遥感监测,以及全国大型湖库透明度遥感监测；在此基础上,开发了地表水环境遥感监测系统,提高了水环境遥感监测效率,促进了卫星遥感在水环境监测中的高精度业务化应用。     

A Bayesian hierarchical approach to quantifying stakeholder attitudes toward conservation in the presence of reporting error

Stakeholder support is vital for achieving conservation success, yet there are few reliable mechanisms to monitor stakeholder attitudes toward conservation. Approaches used to assess attitudes rarely account for bias arising from reporting error, which can lead to falsely reporting a positive attitude toward conservation (false-positive error) or not reporting a positive attitude when the respondent has a positive attitude toward conservation (false-negative error). Borrowing from developments in applied conservation science, we used a Bayesian hierarchical model to quantify stakeholder attitudes as the probability of having a positive attitude toward wildlife notionally (or in abstract terms) and at localized scales while accounting for reporting error. We compared estimates from our model, Likert scores, and naïve estimates (i.e., proportion of respondents reporting a positive attitude in at least 1 question that was only susceptible to false-negative error) with true stakeholder attitudes through simulations. We then applied the model in a survey of tea estate staff on their attitudes toward Asian elephants (Elephas maximus) in the Kaziranga–Karbi Anglong landscape of northeast India. In simulations, Bayesian model estimates of stakeholder attitudes toward wildlife were less biased than naïve estimates or Likert scores. After accounting for reporting errors, we estimated the probability of having a positive attitude toward elephants notionally as 0.85 in the Kaziranga landscape, whereas the proportion of respondents who had positive attitudes toward elephants at a localized scale was 0.50. In comparison, without accounting for reporting errors, naïve estimates of proportions of respondents with positive attitudes toward elephants were 0.69 and 0.23 notionally and at local scales, respectively. False (positive and negative) reporting probabilities were consistently not 0 (0.22–0.68). Regular and reliable assessment of stakeholder attitudes–combined with inference on drivers of positive attitudes–can help assess the success of initiatives aimed at facilitating human behavioral change and inform conservation decision making.     

Optimization of capture–recapture monitoring of elusive species illustrated with a threatened grasshopper

Information on population sizes and trends of threatened species is essential for their conservation, but obtaining reliable estimates can be challenging. We devised a method to improve the precision of estimates of population size obtained from capture–recapture studies for species with low capture and recapture probabilities and short seasonal activity, illustrated with population data of an elusive grasshopper (Prionotropis rhodanica). We used data from 5 capture–recapture studies to identify methodological and environmental factors affecting capture and recapture probabilities and estimates of population size. In a simulation, we used the population size and capture and recapture probability estimates obtained from the field studies to identify the minimum number of sampling occasions needed to obtain unbiased and robust estimates of population size. Based on these results we optimized the capture–recapture design, implemented it in 2 additional studies, and compared their precision with those of the nonoptimized studies. Additionally, we simulated scenarios based on thresholds of population size in criteria C and D of the International Union for Conservation of Nature (IUCN) Red List to investigate whether estimates of population size for elusive species can reliably inform red-list assessments. Identifying parameters that affect capture and recapture probabilities (for the grasshopper time since emergence of first adults) and optimizing field protocols based on this information reduced study effort (−6% to −27% sampling occasions) and provided more precise estimates of population size (reduced coefficient of variation) compared with nonoptimized studies. Estimates of population size from the scenarios based on the IUCN thresholds were mostly unbiased and robust (only the combination of very small populations and little study effort produced unreliable estimates), suggesting capture–recapture can be considered reliable for informing red-list assessments. Although capture–recapture remains difficult and costly for elusive species, our optimization procedure can help determine efficient protocols to increase data quality and minimize monitoring effort.     

便携式GC-MS仪快速测定水中硝基苯及其在应急监测中的应用

采用便携式GC-MS仪快速测定水中硝基苯,通过优化水中离子强度和顶空加热时间,使方法在0μg/L^300μg/L范围内线性良好,方法检出限为2.5μg/L。标准溶液6次测定结果的RSD为7.8%~10.9%,实际水样的加标回收率为80.7%~103%。同步测定试验表明,硝基苯与7种苯系物分离良好。与国标方法对比,该方法单个样品测定时间由2 h缩短为15 min。将该方法用于应急监测工作中,及时有效的数据可为污水处理及事故调查提供分析和研判依据。     

玛纳斯河流域荒漠草地沙化遥感监测研究

以1989—2016年玛纳斯河流域TM/OLI遥感影像为数据源,利用混合像元分解技术,计算玛纳斯河流域草地总覆盖度和裸沙面积。在此基础上通过监测年与基期年的比较,计算草地覆盖度相对基期年的减少率和裸沙面积相对基期年的增加率两个监测指标,依据《天然草地退化、沙化、盐渍化的分级指标》(GB 19377—2003),对计算出的两个指标分别进行沙化等级评定和赋值,将两种评定结果相综合来监测草地沙化。结果表明,玛纳斯河流域近30年来荒漠草地沙漠化总体呈现先增加后降低的趋势。分析表明,玛纳斯河流域草地沙化是人为和自然因素双重作用的结果。