Integrating habitat-masked range maps with quantifications of prevalence to estimate area of occupancy in IUCN assessments

Estimates of species geographic ranges constitute critical input for biodiversity assessments, including those for the International Union for the Conservation of Nature (IUCN) Red List of Threatened Species. Area of occupancy (AOO) is one metric that IUCN uses to quantify a species’ range, but data limitations typically lead to either under- or overestimates (and unnecessarily wide bounds of uncertainty). Fortunately, existing methods in which range maps and land-cover data are used to estimate the area currently holding habitat for a species can be extended to yield an unbiased range of plausible estimates for AOO. Doing so requires estimating the proportion of sites (currently containing habitat) that a species occupies within its range (i.e., prevalence). Multiplying a quantification of habitat area by prevalence yields an estimate of what the species inhabits (i.e., AOO). For species with intense sampling at many sites, presence–absence data sets or occupancy modeling allow calculation of prevalence. For other species, primary biodiversity data (records of a species’ presence at a point in space and time) from citizen-science initiatives and research collections of natural history museums and herbaria could be used. In such cases, estimates of sample prevalence should be corrected by dividing by the species’ detectability. To estimate detectability from these data sources, extensions of inventory-completeness analyses merit development. With investments to increase the quality and availability of online biodiversity data, consideration of prevalence should lead to tighter and more realistic bounds of AOO for many taxonomic groups and geographic regions. By leading to more realistic and representative characterizations of biodiversity, integrating maps of current habitat with estimates of prevalence should empower conservation practitioners and decision makers and thus guide actions and policy worldwide.     

Rapid,recurring, structured survey versus bioblitz for generating biodiversity data and analysis with a multispecies abundance model

A bioblitz inexpensively and quickly generates biodiversity data, but bioblitzes are often conducted with haphazard, unreplicated sampling. Results tend to be taxonomically, geographically, or temporally biased, lack metadata, and consist of lists of observed taxa that do not enable further analyses or correction for imperfect detection. A rapid, recurring, structured survey (RRSS) uses a structured sampling design and temporal and spatial replication to survey randomly selected sites on a conservation property. We participated in a loosely structured bioblitz and a subsequent RRSS at Big Canoe Creek Nature Preserve in Springville (St. Clair County), Alabama (USA) to compare observed richness derived from the 2 survey approaches. The RRSS data structure enabled us to fit models that accounted for imperfect detection to estimate abundances, occupancy probabilities, and habitat associations. The loosely structured bioblitz data could not be used in such models. We present a new integrated multispecies abundance model that we applied to avian RRSS data. Our model extension enables estimation for the community, employs data augmentation to estimate the number of undetected species, and incorporates covariates. The RRSS generated a more comprehensive and less biased list of observed taxonomic richness than the loosely structured bioblitz (e.g., 73 vs. 45 bird species and 104 vs. 63 insect families from the RRSS vs. loosely structured bioblitz, respectively). Models fit to the RRSS data identified seasonal patterns in avian community composition and allowed for estimation of habitat–occupancy relationships for insect taxa. The RRSS protocol has potential for broad transferability as a standardized, quick, and inexpensive way to inventory biodiversity and estimate ecological parameters while providing an outreach opportunity.     

近岸海域水色异常遥感监测方法研究

水体颜色是判断水体质量的重要依据,利用遥感技术检测水色异常是进行水质评估和水体污染监测的重要技术手法。基于盐城市废黄河入海口处的GF1-WFV影像数据,利用遥感影像自身光谱特征来构建水色异常判断函数,对研究区域的水色异常进行快速提取和定位,并比较分析了基于单景影像特征和多景影像特征设置判断阈值的提取结果。研究结果表明：该方法完全基于遥感影像光谱特征,可有效提取出未知类型的水色异常信息;与基于单景影像设置的判断阈值相比,基于双景影像设置的判断阈值更合理、更适用,提取速度更快,提取范围更精确,Kappa系数为0.80;另外,采用合适窗口大小进行均值滤波处理能有效防止提取结果细碎和斑块化。     

模糊聚类在不良地质洞段红外探水中的应用

在玉昆铁路柿花树隧道不良地质发育的洞段进行红外探水工作期间,常用的分析方法有时无法预判掌子面前方是否存在隐伏水体或含水构造。为了提高红外探水短距离预报的准确率,用模糊聚类法将掌子面测点场强的数据分类,根据开挖传递的地质信息,研究模糊聚类结果的变化规律、突水风险与岩体破碎程度之间的关系。实践证明,模糊聚类法和常用分析方法的结合,使红外探水取得了较好的预判效果。     

高校数字图书馆网络安全风险分析与策略

从数字图书馆网络内部和使用服务的外部两方面,分析高校图书馆网络面临的硬件安全风险、软件安全风险、技术风险、管理风险以及外部攻击安全风险等主要风险。结合网络安全技术的发展,分别提出各自的应对策略。重点分析黑客攻击和网络病毒的控制方法,包括采用杀毒防护软件、运用防火墙技术、加密认证技术、入侵检测技术、VLAN技术、内容检查与监控技术以及虚拟蜜罐技术等等。图书馆网络系统只有实现了设备的本质安全化、人员网络技术的专业化以及管理的正规化,才能有效地规避各种网络安全风险和确保网络系统的正常运行,研究结果对如何预防与控制高校数字图书馆网络安全风险具有借鉴和指导作用。     

基于多信息检测的车辆智能防撞预警技术研究

运用多源信息检测技术对驾驶状态进行综合检测,利用架设在车辆外部的摄像机获取本车与前方车辆之间的相对距离、相对速度信息,利用架设在车辆内部的摄像机获取驾驶人状态信息,通过综合判断降低车辆防撞检测的漏警率、误警率以及冗余报警率;构建车辆行驶安全判定规则,对行车实时危险和潜在危险进行检测、分析与判断,获取可靠的防撞警示提醒;使反应时间、相对距离、相对速度3个方面都能得到优化控制,从而避免车辆碰撞事故;同时也为提高车速、增加道路通行能力、实现自动驾驶等奠定良好的基础。     

工作场所有毒有害气体检测报警的法规和技术要求

通过查阅国内外有关有毒有害气体检测报警的法规、标准和文献资料,阐述工作场所有毒气体检测报警的法规和技术要求。分析表明,我国有毒有害气体检测报警法律和配套标准不够完善,部分标准重复、交叉或矛盾,缺乏系统性和可操作性,与欧美发达国家标准有较大差距。因此,应加快技术标准的完善和研制,加强对不同作业场所有毒有害气体检测报警实际应用和主要问题的研究。     

空气中恶臭气体的官能测定研究

本文参照国外资料,结合国内的恶臭污染状况及实验条件,建立了以无臭气为参比的改进三点比较式臭袋法的官能实验方法.本法利用人的嗅觉功能对恶臭空气进行检测,所得结果具有可比性,是一种可靠的、简便的、易推广的方法.     

淄博市中心城区景观水域水质现状分析与改善

通过对淄博市中心城区唯一的景观河——玉龙河的水环境调查分析,对其水温、pH值、色度、DO、BODs、CODMn、氨氮和总磷8项水质指标进行了监测,并对各指标监测结果进行水质单因子评价。结果表明,玉龙河已处于富营养化状态。针对玉龙河水质现状和水环境特征,提出复合生物滤床工艺方案对其进行治理。     

基于吡啶甲基萘酰亚胺的水中痕量铁检测方法研究

合成了一种新型的荧光分子探针N-(2-吡啶甲基)-1,8-萘酰亚胺(PMN),研究了其在环境水样中痕量铁离子的检测应用.结果表明,该方法选择性好,灵敏度高,操作简便,且常见金属离子不干扰测定.Fe3+浓度在1×10-7~8×10-5mol/L范围内,荧光强度与其浓度呈良好的线性关系.方法用于自来水、井水、生活污水中痕量铁的测定,回收率在95.78%~104.02 %之间.