Quantifying population declines based on presence‐only records for red‐list assessments

Accurate trend estimates are necessary for understanding which species are declining and which are most in need of conservation action. Imperfect species detection may result in unreliable trend estimates because this may lead to the overestimation of declines. Because many management decisions are based on population trend estimates, such biases could have severe consequences for conservation policy. We used an occupancy‐modeling framework to estimate detectability and calculate nationwide population trends for 14 Swiss amphibian species both accounting for and ignoring imperfect detection. Through the application of International Union for Conservation of Nature Red List criteria to the different trend estimates, we assessed whether ignoring imperfect detection could affect conservation policy. Imperfect detection occurred for all species and detection varied substantially among species, which led to the overestimation of population declines when detectability was ignored. Consequently, accounting for imperfect detection lowered the red‐list risk category for 5 of the 14 species assessed. We demonstrate that failing to consider species detectability can have serious consequences for species management and that occupancy modeling provides a flexible framework to account for observation bias and improve assessments of conservation status. A problem inherent to most historical records is that they contain presence‐only data from which only relative declines can be estimated. A move toward the routine recording of nonobservation and absence data is essential if conservation practitioners are to move beyond this toward accurate population trend estimation.     

Effectiveness of Scat‐Detection Dogs in Determining Species Presence in a Tropical Savanna Landscape

Abstract: Most protected areas are too small to sustain populations of wide‐ranging mammals; thus, identification and conservation of high‐quality habitat for those animals outside parks is often a high priority, particularly for regions where extensive land conversion is occurring. This is the case in the vicinity of Emas National Park, a small protected area in the Brazilian Cerrado. Over the last 40 years the native vegetation surrounding the park has been converted to agriculture, but the region still supports virtually all of the animals native to the area. We determined the effectiveness of scat‐detection dogs in detecting presence of five species of mammals threatened with extinction by habitat loss: maned wolf (Chrysocyon brachyurus), puma (Puma concolor), jaguar (Panthera onca), giant anteater (Myrmecophaga tridactyla), and giant armadillo (Priodontes maximus). The probability of scat detection varied among the five species and among survey quadrats of different size, but was consistent across team, season, and year. The probability of occurrence, determined from the presence of scat, in a randomly selected site within the study area ranged from 0.14 for jaguars, which occur primarily in the forested areas of the park, to 0.91 for maned wolves, the most widely distributed species in our study area. Most occurrences of giant armadillos in the park were in open grasslands, but in the agricultural matrix they tended to occur in riparian woodlands. At least one target species occurred in every survey quadrat, and giant armadillos, jaguars, and maned wolves were more likely to be present in quadrats located inside than outside the park. The effort required for detection of scats was highest for the two felids. We were able to detect the presence for each of five wide‐ranging species inside and outside the park and to assign occurrence probabilities to specific survey sites. Thus, scat dogs provide an effective survey tool for rare species even when accurate detection likelihoods are required. We believe the way we used scat‐detection dogs to determine the presence of species can be applied to the detection of other mammalian species in other ecosystems.     

川东北低山丘陵区细颗粒物主要相关因素和季节变化分析——以南充主城区为例

基于中国空气质量在线监测分析平台和全球天气精准预报网的大气质量和气象数据,以四川盆地东北低山丘陵区典型城市南充市主城区为例,检验了细颗粒物(PM_(2.5))浓度的概率密度分布,发现其接近对数正态分布,由相关分析确定了PM_(2.5)浓度的主要相关因素为CO、NO_2(相关系数r分别为0.76、0.55,P0.01),再通过对2014年1月—2016年6月的日数据的逐步回归筛选出最优的回归指标和模拟方程(决定系数R_(adj)~2为0.68,P0.05),2016年7月—2017年6月的数据验证表明模拟效果较好(拟合优度为0.64,相对误差15.48%);最后根据时序插值、浓度和IAQI(PM_(2.5))的时段均值发现PM_(2.5)浓度在年际上有降低趋势;在季节上由高到低依次为冬季、春季、秋季、夏季;PM_(2.5)浓度在1月和6月分别呈现出年内的峰值和谷值,5、10月出现了阶段性峰值,尤其是5月;IAQI(PM_(2.5))的季节变化与浓度变化规律相似;且PM_(2.5)与PM_(10)比值的均值为0.67,表明现阶段南充市主城区大气污染物中细颗粒物占有较大比重。     

基于改进RPS的共因失效模型及在安全仪表系统中的应用研究

基于安全仪表系统共因失效机理的定性分析,在单一冲击下的RPS模型基础上,以泊松分布作为冲击的先验分布,建立了多冲击下考虑共因失效的安全仪表系统可靠性模型;采用基于专家经验和估计的方法,对模型参数进行估计;并通过定量分析确定出影响系统共因失效的最大冲击因子,为降低系统共因失效对可靠性的影响提供辅助决策支持。将改进的多冲击RPS模型的可靠性分析结果与β模型和PDS模型进行比较,验证了该模型的工程准确性。     

基于均匀分布的滑坡数值模拟参数取值概率研究--以甘肃黑方台黄土滑坡为例

针对数值模拟参数选取主观性强、量化难度大的问题,在高精度无人机地形数据的基础上,通过对11起滑坡案例进行了407组参数反演实验,对结果准确度定量评价后,得到基于Massflow数值软件关键参数λ0的分布范围,进而运用小样本极大似然估计理论,分析区间边界长度对反演精度的影响,最终提出基于均匀分布的参数取值概率模型,并选用案例验证该模型的准确性。结果表明:地形约束会导致反演过程中所需的内聚力减小,反演获得的11组最优λ0的极差和方差分别为0.29、0.01,在置信度为95%下,极大似然法得到区间边界估计长度仅为0.0998,表明参数λ收敛性较好;反演过程中质心滑动距离ψ准确度优于堆积面积重叠率η,在λ0±0.05的范围,参数估计区间内任意值对模拟误差影响较小,评价指标ψ、η与λ0对应案例的相对误差不超过15%;所选案例验证了在置信度为95%下,以反演得到的最优参数区间边界中点构建概率分布函数的可行性和准确性,研究方法可为单体滑坡数值模拟风险评价提供理论支撑。     

赤泥碱性调控研究进展

赤泥是氧化铝工业生产过程产生的强碱性固体废弃物,资源化利用难、环境风险高,严重制约了氧化铝行业的可持续发展.赤泥土壤化是实现规模化处置赤泥的一种可行方法,而碱性调控则是赤泥土壤化的关键环节.论文在综述氧化铝生产过程碱性物质形成过程的基础上,从可溶性碱和化学结合碱角度分析了赤泥碱性物质的赋存状态,阐述了国内外化学调碱法和生物调碱法的研究进展和碱性转化机制,剖析了赤泥碱性调控方面存在的问题,提出了赤泥碱性调控研究的发展方向.这将为赤泥规模化处置和堆场生态重建、保障氧化铝工业的健康发展提供科学参考.     

淮南深部山西组煤中汞的含量分布及赋存特征研究

以淮南煤田深部山西组煤为研究对象,分别采集研究区张集煤矿和新集二矿二叠系山西组1煤层的煤样品,测试了样品中总汞的含量,分析汞的空间分布特征,采用浮沉实验、逐级化学提取实验以及相关性分析研究了煤中汞的赋存状态,并结合沉积环境探讨了深部山西组煤中汞的富集成因.结果 表明:(1)研究区煤样中汞含量介于0.03～0.93 μg...     

水动力条件下蓝藻水华生消的模拟实验研究与探讨

蓝藻水华是当今世界共同面临的重大水污染问题之一。通过室内模拟实验,在温度、光照、初始pH值和营养盐等基本条件相同的前提下模拟水流流速分别为10cm/s、20cm/s、30cm/s、40cm/s时铜绿微囊藻(Microcystis aeruginosa)水华的产生与消亡过程,在10～40cm/s流速区间里藻类生长周期随流速增大而变长,藻类最大现存量在40cm/s流速下最大,在10cm/s流速下最小,流速为30cm/s时藻类比增率最大,较适合藻类生存,在整个水华暴发过程中水体氮磷营养浓度呈下降趋势,水体的pH、DO和Ec变化不大。     

基于双指标多等级的土壤重金属生态风险评价

采用土壤中重金属的全量和有效态双重指标,建立基于多等级综合评估的土壤中重金属生态风险评价模型,将联合概率曲线法引入土壤评价模型,分析重金属暴露浓度与毒性数据的概率分布,考察重金属对土壤生物的毒害程度,从而确定土壤中重金属对于生态系统的风险。建立从简单到复杂的多等级综合评价方法,表征重金属的污染等级、浓度效应、多种重金属污染物的协同效应、不同重金属的毒性效应和土壤对不同重金属污染物的敏感性。选择典型地区采集有代表性的土壤样品,测定不同重金属的总量和有效态,验证评价模型的实用性和评价分级的合理性。旨在解决土壤重金属风险评价的方法学问题,为土壤环境质量管理提供支持。     

巢湖水及沉积物中总磷的分布变化特征

磷是导致巢湖水体富营养化的主要营养物质。采集大量巢湖表层水和沉积物样品,通过检测上覆水和沉积物中总磷含量,分析巢湖水体中磷的时空变化及赋存特征。结果显示:巢湖南淝河和裕溪河河口的上覆水中总磷含量值时间变化特征为8月5月3月12月;且南淝河口总磷含量年均值超过地表水Ⅴ类水质标准,明显高于裕溪河口值;表层水和沉积物中总磷含量在空间分布上呈西高东低趋势,最高值均出现在靠近合肥市河口处。巢湖周边土壤及湖区磷的等值线分布表明:杭埠河流域农业污染、东巢湖东南部水土流失可能是巢湖磷面源污染的主要来源。巢湖上覆水和沉积物中总磷的相关系数为0.515,蓝藻爆发期全湖表层沉积物中总磷含量显著减少,揭示目前内源磷释放已是巢湖富营养化的主要因素。结果将对巢湖流域的污染综合防治及蓝藻治理工作提供科学依据。