Valuing biodiversity: reality or mirage?

The objective of this paper was to consider the social value ofbiological diversity and explore if this value could be expressedin terms of a unidimensional metric in money. Economics distinguishes between use-values and non-use-values, which are critically evaluated for valuing biodiversity. It is shown that these utility-based valuations have severe limitations as they treat species in isolation from their ecological contexts. In contrast, ecosystem ecology regards ecosystems as an integratednon-linear and nonconvex system in which ecosystem functions canbe understood as a four-component cycle; exploitation, accumulation of biomass, creative destruction and renewal. Withinsuch a cycle, ecosystems can be seen to have two properties: stability and resilience. A good proxy for resilience is the probability of extinction of species, and social value of biodiversity can be expressed as a partial ordering with thisprobability as an index. This approach is consistent with decision theory, of which social choice is an important component, pioneered by Arrow.     

Threatened Species and the Potential Loss of Phylogenetic Diversity: Conservation Scenarios Based on Estimated Extinction Probabilities and Phylogenetic Risk Analysis

Abstract: New species conservation strategies, including the EDGE of Existence (EDGE) program, have expanded threatened species assessments by integrating information about species' phylogenetic distinctiveness. Distinctiveness has been measured through simple scores that assign shared credit among species for evolutionary heritage represented by the deeper phylogenetic branches. A species with a high score combined with a high extinction probability receives high priority for conservation efforts. Simple hypothetical scenarios for phylogenetic trees and extinction probabilities demonstrate how such scoring approaches can provide inefficient priorities for conservation. An existing probabilistic framework derived from the phylogenetic diversity measure (PD) properly captures the idea of shared responsibility for the persistence of evolutionary history. It avoids static scores, takes into account the status of close relatives through their extinction probabilities, and allows for the necessary updating of priorities in light of changes in species threat status. A hypothetical phylogenetic tree illustrates how changes in extinction probabilities of one or more species translate into changes in expected PD. The probabilistic PD framework provided a range of strategies that moved beyond expected PD to better consider worst‐case PD losses. In another example, risk aversion gave higher priority to a conservation program that provided a smaller, but less risky, gain in expected PD. The EDGE program could continue to promote a list of top species conservation priorities through application of probabilistic PD and simple estimates of current extinction probability. The list might be a dynamic one, with all the priority scores updated as extinction probabilities change. Results of recent studies suggest that estimation of extinction probabilities derived from the red list criteria linked to changes in species range sizes may provide estimated probabilities for many different species. Probabilistic PD provides a framework for single‐species assessment that is well‐integrated with a broader measurement of impacts on PD owing to climate change and other factors.     

Environmental Synergisms and Extinctions of Tropical Species

Abstract: Environmental synergisms may pose the greatest threat to tropical biodiversity. Using recently updated data sets from the International Union for Conservation of Nature (IUCN) Red List, we evaluated the incidence of perceived threats to all known mammal, bird, and amphibian species in tropical forests. Vulnerable, endangered, and extinct species were collectively far more likely to be imperiled by combinations of threats than expected by chance. Among 45 possible pairwise combinations of 10 different threats, 69%, 93%, and 71% were significantly more frequent than expected for threatened mammals, birds, and amphibians, respectively, even with a stringent Bonferroni‐corrected probability value (p= 0.003). Based on this analysis, we identified five key environmental synergisms in the tropics and speculate on the existence of others. The most important involve interactions between habitat loss or alteration (from agriculture, urban sprawl, infrastructure, or logging) and other anthropogenic disturbances such as hunting, fire, exotic‐species invasions, or pollution. Climatic change and emerging pathogens also can interact with other threats. We assert that environmental synergisms are more likely the norm than the exception for threatened species and ecosystems, can vary markedly in nature among geographic regions and taxa, and may be exceedingly difficult to predict in terms of their ultimate impacts. The perils posed by environmental synergisms highlight the need for a precautionary approach to tropical biodiversity conservation.     

Using Scalar Models for Precautionary Assessments of Threatened Species

Abstract:  Scalar population models, commonly referred to as count-based models, are based on time-series data of population sizes and may be useful for screening-level ecological risk assessments when data for more complex models are not available. Appropriate use of such models for management purposes, however, requires understanding inherent biases that may exist in these models. Through a series of simulations, which compared predictions of risk of decline of scalar and matrix-based models, we examined whether discrepancies may arise from different dynamics displayed due to age structure and generation time. We also examined scalar and matrix-based population models of 18 real populations for potential patterns of bias in population viability estimates. In the simulation study, precautionary bias (i.e., overestimating risks of decline) of scalar models increased as a function of generation time. Models of real populations showed poor fit between scalar and matrix-based models, with scalar models predicting significantly higher risks of decline on average. The strength of this bias was not correlated with generation time, suggesting that additional sources of bias may be masking this relationship. Scalar models can be useful for screening-level assessments, which should in general be precautionary, but the potential shortfalls of these models should be considered before using them as a basis for management decisions.     

基于CALIOP探测的京津冀地区气溶胶垂直分布特征

利用2011—2015年星载激光雷达(CALIOP)探测资料分析京津冀地区气溶胶消光系数(AEC)的垂直分布,AEC表征了气溶胶的浓度水平。结果表明:AEC的垂直分布季节变化显著,2 km以下尤为明显;整层气溶胶光学厚度(AOD)在夏季(0.7)高于其他季节,湿度对AEC产生较大的影响;而500 m以下低层的AEC体现的特征与之相反,冬季最大(0.65 km~(-1)),春夏季较小(0.25 km~(-1))。夏秋季低层AEC存在较大昼夜差异,夜间最大为0.65 km~(-1),是白天的2.5倍,低层较大的相对湿度昼夜变化成为消光系数巨大差异的主因。南北向贯穿京津冀地区垂直剖面上AEC的分布显示,在夏季夜间,气溶胶浓度水平在南部地区的低层较高,而在其他季节并没有体现出明显的南北差异。     

西安泾河夏季黑碳气溶胶及其吸收特性的观测研究

为研究西安泾河夏季黑碳气溶胶及其吸收特性,利用2011年夏季西安远郊泾河大气成分站观测的黑碳气溶胶浓度、颗粒物质量浓度、探空资料、地面气象资料,计算边界层顶高度、气溶胶吸收系数、大气消光系数,导出单次散射反照率,并对其进行分析讨论.结果表明:西安夏季黑碳气溶胶浓度为6.07μg/m3;黑碳气溶胶占颗粒物质量浓度PM1.0比值为21.9%,黑碳气溶胶与颗粒物质量浓度PM1.0、PM2.5、PM10相关系数分别为0.69、0.85、0.91;黑碳气溶胶浓度受城市边界层顶高度影响,风向、风速对泾河黑碳气溶胶的堆积输送有不同作用;气溶胶吸收系数和大气消光系数日变化显著,气溶胶吸收系数占大气消光系数比值范围在12%~30%;季单次散射反照率平均值为0.76,变化范围在0.70~0.84.     

冬季德州市大气颗粒物消光与化学组成关系研究

为了深入探究华北地区冬季大气颗粒物的消光特性和化学组分之间的关系,本研究于2017年11月—2018年1月在山东省德州市平原县对大气颗粒物消光和化学组成进行了连续在线观测.运用多元线性回归方法和MIE散射模型定量分析了颗粒物各化学组成对颗粒物消光的贡献,进一步地,利用高分辨飞行时间气溶胶质谱(HR-ToF-AMS)结合正矩阵因子解析模型(PMF)得出二次气溶胶(OOA)、生物质燃烧有机气溶胶(BBOA)、还原性气溶胶(HOA)、燃煤燃烧排放的有机气溶胶(CCOA)的浓度,并进一步结合线性回归模型得到OOA、BBOA、HOA、CCOA对消光的贡献.结果显示元素碳(EC)是颗粒物吸光的最主要贡献者, OOA、BBOA和CCOA对颗粒物吸光也具有一定贡献,这主要是由于二次生成和一次排放的棕色碳的吸光造成的.颗粒物各组分与散射关系的分析结果表明,有机物对颗粒物散射影响最大,其中OOA的散射截面最大,对颗粒物散射的贡献也最高,可以占到总散射的53.4%.颗粒物中有机物对大气总消光的贡献可达75.5%,其中OOA、BBOA、CCOA和HOA对总消光的贡献分别为47.8%、14.7%、9.0%、4.0%. Mie散射的结果与多元回归结果比较一致,但部分时间段偏差较大,因此在不同的研究中应根据不同情况选择研究方法.     

浙江省气溶胶垂直分布及区域传输分析

利用6个激光雷达站点数据从年际和季节变化尺度对浙江省2017~2018年气溶胶垂直分布特征进行研究,并结合污染物浓度数据分析了浙江省主要颗粒物类型的季节差异.结果表明,大部分站点2018年气溶胶消光系数整体小于2017年;边界层内消光系数空间上呈中部高东西低的分布形态;消光系数季节变化表现为冬季最高,秋季其次,而春夏较低;气溶胶在春、夏季集中在地面上方1~2km以内,冬季主要分布在1km以下,秋季受高空气溶胶影响,6个站点呈现出不同的垂直分布模式.进一步,选取发生在2018年4月和7月两次气溶胶跨区域传输过程,综合CALIPSO和MODIS卫星数据、以及后向轨迹、PSCF与CWT模式对污染物来源与传输机制进行探索,发现来自AOD高值区的气团显著加重本地污染.     

Humidity and PM2.5 composition determine atmospheric light extinction in the arid region of northwest China

Atmospheric visibility can directly reflect the air quality. In this study, we measured water-soluble ions (WSIs), organic and element carbon (OC and EC) in PM_2.5 from September 2017 to August 2018 in Urumqi, NW China. The results show that SO₄^2?, NO₃^? and NH₄⁺ were the major WSIs, together accounting for 7.32%–84.12% of PM_2.5 mass. Total carbon (TC=OC+EC) accounted for 12.12% of PM_2.5 mass on average. And OC/EC > 2 indicated the formation of secondary organic carbon (SOC). The levels of SO₄^2?, NO₃^? and NH₄⁺ in low visibility days were much higher than those in high visibility days. Relative humidity (RH) played a key role in affecting visibility. The extinction coefficient (b_ext) that estimated via Koschmieder formula with visibility was the highest in winter (1441.05 ± 739.95 Mm^?1), and the lowest in summer (128.58 ± 58.00 Mm^?1). The b_ext that estimated via IMPROVE formula with PM_2.5 chemical component was mainly contributed by (NH₄)₂SO₄ and NH₄NO₃. The b_ext values calculated by both approaches presented a good correlation with each other (R² = 0.87). Multiple linear regression (MLR) method was further employed to reconstruct the empirical regression model of visibility as a function of PM_2.5 chemical components, NO₂ and RH. The results of source apportionment by Positive Matrix Factorization (PMF) model showed that residential coal combustion and vehicle emissions were the major sources of b_ext.     

廊坊市夏季大气气溶胶消光特性及其来源

通过对廊坊市2016年5~9月的空气污染指标进行连续观测,分析了PM_2.5污染特征、消光特性,并进行来源解析及潜在污染区域分析.观测期间PM_2.5浓度为（43.82±28.68）μg/m,PM_2.5中SO₄^2-,NO₃^-,NH₄⁺,OC和EC分别占PM_2.5总质量的24.74%,22.98%,20.54%,8.79%和5.50%,各组分随着PM_2.5浓度增加而增加;气溶胶散射系数（Bsp）和吸收系数（Bap）分别为（294.54±257.35）,（16.05±9.14）Mm^-1,粗粒子（CM）对消光系数的贡献为11.12%,细颗粒子PM_2.5在大气消光中起主要作用,其中硝酸盐（32.23%）,硫酸盐（27.28%）和OM （20.56%）为最主要消光成分;PM_2.5主要来自工业（14.14%）、机动车（15.15%）、二次无机气溶胶（38.38%）、燃煤（22.22%）及扬尘和生物质燃烧（10.10%）,对消光系数的贡献分别为工业（1.32%）、机动车（17.25%）、二次气溶胶（55.57%）、燃煤（18.56%）,扬尘和生物质燃烧（7.32%）.后轨迹分析表明廊坊市以来自山东、河南的小尺度_,短距离和来自内蒙古东北部、中部的中短距离传输为主,占比接近80%,污染源的潜在分布区域主要是山东东北部、河南东部、山西东部、辽宁西南等地.