Moving from trends to benchmarks by using regression tree analysis to find inbreeding thresholds in a critically endangered bird

Understanding how inbreeding affects endangered species in conservation breeding programs is essential for their recovery. The Hawaiian Crow (‘Alalā) (Corvus hawaiiensis) is one of the world's most endangered birds. It went extinct in the wild in 2002, and, until recent release efforts starting in 2016, nearly all of the population remained under human care for conservation breeding. Using pedigree inbreeding coefficients (F), we evaluated the effects of inbreeding on Hawaiian Crow offspring survival and reproductive success. We used regression tree analysis to identify the level of inbreeding (i.e., inbreeding threshold) that explains a substantial decrease in ‘Alalā offspring survival to recruitment. Similar to a previous study of inbreeding in ‘Alalā, we found that inbreeding had a negative impact on offspring survival but that parental (vs. artificial) egg incubation improved offspring survival to recruitment. Furthermore, we found that inbreeding did not substantially affect offspring reproductive success, based on the assumption that offspring that survive to adulthood breed with distantly related mates. Our novel application of regression tree analysis showed that offspring with inbreeding levels exceeding F = 0.098 were 69% less likely to survive to recruitment than more outbred offspring, providing a specific threshold value for ongoing population management. Our results emphasize the importance of assessing inbreeding depression across all life history stages, confirm the importance of prioritizing parental over artificial egg incubation in avian conservation breeding programs, and demonstrate the utility of regression tree analysis as a tool for identifying inbreeding thresholds, if present, in any pedigree-managed population.     

Using fisher-contributed secondary fins to fill critical shark-fisheries data gaps

Developing-world shark fisheries are typically not assessed or actively managed for sustainability; one fundamental obstacle is the lack of species and size-composition catch data. We tested and implemented a new and potentially widely applicable approach for collecting these data: mandatory submission of low-value secondary fins (anal fins) from landed sharks by fishers and use of the fins to reconstruct catch species and size. Visual and low-cost genetic identification were used to determine species composition, and linear regression was applied to total length and anal fin base length for catch-size reconstruction. We tested the feasibility of this approach in Belize, first in a local proof-of-concept study and then scaling it up to the national level for the 2017–2018 shark-fishing season (1,786 fins analyzed). Sixteen species occurred in this fishery. The most common were the Caribbean reef (Carcharhinus perezi), blacktip (C. limbatus), sharpnose (Atlantic [Rhizoprionodon terraenovae] and Caribbean [R. porosus] considered as a group), and bonnethead (Sphyrna cf. tiburo). Sharpnose and bonnethead sharks were landed primarily above size at maturity, whereas Caribbean reef and blacktip sharks were primarily landed below size at maturity. Our approach proved effective in obtaining critical data for managing the shark fishery, and we suggest the tools developed as part of this program could be exported to other nations in this region and applied almost immediately if there were means to communicate with fishers and incentivize them to provide anal fins. Outside the tropical Western Atlantic, we recommend further investigation of the feasibility of sampling of secondary fins, including considerations of time, effort, and cost of species identification from these fins, what secondary fin type to use, and the means with which to communicate with fishers and incentivize participation. This program could be a model for collecting urgently needed data for developing-world shark fisheries globally. Article impact statement: Shark fins collected from fishers yield data critical to shark fisheries management in developing nations.     

关于大气中PM2.5浓度的综合预测

鉴于PM2.5对人体的危害，为了提前预测PM2.5浓度值并及时通过降雨方法降低其浓度，开展了PM2.5浓度综合预测研究。通过对西安市一个空气质量监测点收集到观测数据分析与PM2.5浓度强相关的因素，随后综合考虑天气、温度、风力风向对PM2.5浓度观测值的影响，通过添加虚拟变量及因子分析提取因子的方法，将强相关因素、提取的天气、温度、风力风向因子与PM2.5浓度观测值进行回归预测，最终得到较好预测效果。     

Societal risk acceptance criteria for gas distribution pipelines based on incident data from the United States

A significant number of pipeline operators use pipeline integrity management (PIM) to improve pipeline safety and reliability. Risk assessment is a critical step in PIM, because it determines the necessity of conducting the following steps in PIM for certain pipelines. Risk acceptance criteria are required in the process of risk assessment. Individual risk and societal risk are most frequently adopted as the two indicators of the risk acceptance criteria. To the best of the authors’ knowledge, quantitative societal risk acceptance criteria, especially for gas distribution pipelines, do not exit. The aim of this paper is to establish the societal risk acceptance criteria for gas distribution pipelines. Hence, FN curves were established using historical incident data from 2002 to 2017 provided by the U.S. Department of Transportation (DOT). Linear regression and the ALARP principle are used in evaluating the limits of the negligible line and intolerable line to obtain a graphical societal risk acceptance criterion for gas distribution pipelines. A line having a slope of −1.224, and an anchor point of (1, 8.413 × 10⁻⁷) is proposed as the negligible line. Further, the intolerable line has a slope of −1.224, and an anchor point of (1, 2.524 × 10⁻⁶). Both the negligible risk and the intolerable risk for the gas distribution pipeline are lower than the current societal risk acceptance criteria for hazardous installations. The reasons for these relatively lower risk acceptance criteria are discussed.     

Predictive model for areas with illegal landfills using logistic regression

The existence of illegal landfills is an environmental problem in most countries. However, research on this issue is scarce and limited by the availability and quality of data on the subject. Thus, most illegal landfill studies have only been conducted in a partial manner, focusing on geographical aspects or the causes of these landfills (lack of environmental awareness, inadequate waste management systems, and the role of local government). This research analyses a sample of 120 possible areas with illegal landfills in Andalusia using logistic regression in order to obtain a predictive model for the occurrence of these landfills, including both types of variables (geographical and behavioural) jointly. The results confirm that the variables that most influence the occurrence of illegal landfills are spatial (“Industrial Land”, “Plains” and “Rural Land”); whilst the variables that most reduce the likelihood of illegal landfills are those related to certain characteristics of the municipal waste management system and environmental awareness, such as “Availability of Recycling Facilities”, “Punitive Policies”, “Supervision” and “Awareness-raising Campaigns”. The model obtained shows that variables of very different nature and magnitude interact in the occurrence of illegal landfills, each of which contributes a series of features characteristic of its scale. It is advisable, therefore, to perform an analysis using a multi-scale approach in order to gain an overall understanding of the phenomenon.     

基于支持向量回归模型的水稻田甲烷排放通量预测研究

利用静态箱和气相色谱仪法获取水稻田甲烷排放通量数据,选取大气温度、土壤5 cm深温度、土壤pH、土壤Eh、土壤含水量和地表生物量作为影响因子.应用建立在结构风险最小化优化上的支持向量回归(ε-SVR)模型,采用留一法交叉检核网格搜索法(LOOCV)优化ε-SVR预测模型的参数,采用k折交叉检验的方法依据平均相对误差(MRE)和均方根误差(RMSE)对模型的精度进行验证,并与BP人工神经网络(BP-ANN)模型比较,评价ε-SVR预测模型的准确性.结果表明,通过LOOCV选择最优的惩罚因子C和损失系数ε,并由此构建的ε-SVR预测模型预测值和实测值具有很好的一致性,通过11折交叉验证后,测试样本的平均MRE为44%,平均RMSE为16.21 mg·(m2·h)-1.通过与BP-ANN模型比较,预测值和实际值相关系数达0.863,各项指标均优于BP-ANN预测模型,说明ε-SVR模型能够适用于水稻田甲烷排放通量的预测.     

中田河流域景观异质性对水体总氮浓度影响研究

土地利用变化使输移至河流的营养盐负荷发生变化从而导致地表水水质变化.研究景观异质性对营养负荷的影响程度对改善水质的最佳管理措施至关重要.本研究从2013年1~12月,对中田河流域干支流水系20个监测点的地表水总氮浓度进行了周年定点监测,考虑到流域单元的嵌套关系,选择7个土地利用结构和13个景观格局指数,探讨了景观异质性对中田河流域水体总氮浓度的影响.研究表明:1建设用地和草地对总氮浓度有重大影响,表现为建设用地会恶化水质,而草地可以改善水质;2景观斑块-形状指数、景观斑块-面积指数和建设用地是景观尺度上影响流域总氮浓度的主要因素;3回归分析进一步显示建设用地和景观斑块-面积指数对总氮浓度的贡献分别是67.31%和32.69%.     

西北旱区盐湖盆地地下水化学组分源解析

为实现对吉兰泰盐湖盆地地下水污染源的识别与管理,系统采集区域内71个地下水样品,测定16项地下水质关键指标;以GB/T 14848—2017《地下水质量标准》中的Ⅲ类标准为依据确定特征污染物,利用因子分析（FA）确定地下水水质指标的因子分类,以地质统计学插值绘图揭示不同污染源的空间分布特征,运用APCS-MLR（绝对主成分得分多元线性回归）量化不同污染源的贡献率.结果表明:研究区内ρ（Cr6+）、ρ（As）、ρ（NH₄+）、ρ（F-）、ρ（Cl-）、ρ（NO₂-）、ρ（COD_Mn）、ρ（TDS）、pH等9项地下水水质指标均存在超标现象,其中ρ（NH₄+）、ρ（Cl-）、ρ（F-）超标较为严重.通过因子分析法筛选出影响研究区地下水水质的6个公因子,即溶滤-富集作用因子（F₁,贡献率为24.61%）、农业活动因子（F₂,贡献率为20.38%）、原生地质-农业生产、生活污染因子（F₃,贡献率为11.72%）、工业生产污染因子（F₄,贡献率为10.38%）、地质环境背景因子（F₅,贡献率为10.78%）、原生地质因子（F₆,贡献率为10.61%）,其中F₁、F₅、F₆为环境影响因子,F₂、F₃、F₄为人类活动影响因子.采用因子得分函数计算得到因子得分,巴音乌拉山一带整体污染因子得分较高,乌兰布和沙漠存在点状高值区,图格力高勒沟谷上游也存在一定程度的污染,而盐湖盆地东南大部分区域水质相对较好,其分布与变化受到天然因素和人类活动的双重影响.利用APCS-MLR得到各水质指标预测值与实测值的R2（线性拟合优度）均大于0.7,APCS-MLR可较好地评估各因子对水质的贡献率.研究显示,因子分析与APCS-MLR相结合可以有效地对地下水化学组分进行定性识别与定量解析.      

城市交通道路氮氧化物浓度的CART回归树预测研究

提出了基于CART回归树的氮氧化物(NO_x)浓度预测模型,利用杭州市延安路路边空气质量监测站2016年6—9月空气污染物监测数据和同期延安路路段车辆抓拍识别数据,通过数据处理、影响因素分析及CART回归树构造,搭建了NO_x浓度预测模型.实验分析结果表明,相对于支持向量机和BP神经网络预测模型,基于CART回归树的NO_x浓度预测模型的预测精度有大幅度提升,可决系数在0.92以上;同时,对环境条件差异较大的G20会议期间NO_x浓度进行预测分析,结果表明,CART回归树方法的预测精度比其它方法更高,能够适应不同条件下的预测需求.     

鄱阳湖碟形湖泊（常湖池）春季苔草生物量遥感估算

基于Sentinel-2植被指数,应用回归分析法分析了鄱阳湖碟形子湖泊（常湖池）的苔草（Carex）群落生物量与植被指数的关系,并探讨了高程水位和气温变化对其的影响。研究结果表明：（1）10种常用的植被指数中,土壤调节植被指数（Soil Adjusted Vegetation Index,SAVI）是常湖池苔草春季生长中后期（3月22日至5月5日）地上生物量估算的最佳植被指数,SAVI构建的三次多项式是常湖池苔草地上生物量最优遥感估算模型,其均方根误差为73.91 g/m²,预测吻合度为71.90%,苔草生物量分布总体表现为自湖心到湖岸逐渐增加。（2）3月22日（春季苔草生长中期）和5月5日（春季苔草生长后期）苔草的地上总生物量分别为1.06×10⁵ kg和3.28×10⁵ kg,单位面积苔草生物量分别为77.56 g/m²和208.44 g/m²,这与鄱阳湖其他子湖单位面积生物量一般低于300 g/m²相一致。（3）常湖池苔草生长受高程、水位和气温多重要素综合影响。3月底常湖池13.47 m高程（黄海高程,参考星子站水位,下同）以下苔草植株矮小,生物量积累较少;13.47 m高程以上区域受前期低温胁迫,生物量增长缓慢。随着气温回暖,出露区域的苔草生物量逐渐积累,并随高程增加而增长。