洪涝灾害评价的威布尔模型

首次将威布尔分布用于淮河流域水灾成灾面积研究,揭示了淮河流域洪涝灾害成灾面积形成的 内在规律,进而利用河南、安徽、江苏和山东4省的各自灾度对淮河流域的灾害风险建立了线性回归模型.实证分析表明,本方法切实可行,特别适用于大样本计算.     

北京市地热资源开发的时空变化

简要介绍了北京地热资源特点,分析了北京地热资源开发的时空演变规律.在搜集资料和实地调查的基础上,绘制出了北京城区地热井分布示意图,并针对其开发利用中可能出现的问题提出了对策.     

Spatial Patterns of Breeding Success of Grizzly Bears Derived from Hierarchical Multistate Models

Conservation programs often manage populations indirectly through the landscapes in which they live. Empirically, linking reproductive success with landscape structure and anthropogenic change is a first step in understanding and managing the spatial mechanisms that affect reproduction, but this link is not sufficiently informed by data. Hierarchical multistate occupancy models can forge these links by estimating spatial patterns of reproductive success across landscapes. To illustrate, we surveyed the occurrence of grizzly bears (Ursus arctos) in the Canadian Rocky Mountains Alberta, Canada. We deployed camera traps for 6 weeks at 54 surveys sites in different types of land cover. We used hierarchical multistate occupancy models to estimate probability of detection, grizzly bear occupancy, and probability of reproductive success at each site. Grizzly bear occupancy varied among cover types and was greater in herbaceous alpine ecotones than in low‐elevation wetlands or mid‐elevation conifer forests. The conditional probability of reproductive success given grizzly bear occupancy was 30% (SE = 0.14). Grizzly bears with cubs had a higher probability of detection than grizzly bears without cubs, but sites were correctly classified as being occupied by breeding females 49% of the time based on raw data and thus would have been underestimated by half. Repeated surveys and multistate modeling reduced the probability of misclassifying sites occupied by breeders as unoccupied to <2%. The probability of breeding grizzly bear occupancy varied across the landscape. Those patches with highest probabilities of breeding occupancy—herbaceous alpine ecotones—were small and highly dispersed and are projected to shrink as treelines advance due to climate warming. Understanding spatial correlates in breeding distribution is a key requirement for species conservation in the face of climate change and can help identify priorities for landscape management and protection. Patrones Espaciales del Éxito Reproductivo de Osos Pardos, Derivados de Modelos Jerárquicos Multi‐Estado     

Applying network theory to prioritize multispecies habitat networks that are robust to climate and land‐use change

Designing connected landscapes is among the most widespread strategies for achieving biodiversity conservation targets. The challenge lies in simultaneously satisfying the connectivity needs of multiple species at multiple spatial scales under uncertain climate and land‐use change. To evaluate the contribution of remnant habitat fragments to the connectivity of regional habitat networks, we developed a method to integrate uncertainty in climate and land‐use change projections with the latest developments in network‐connectivity research and spatial, multipurpose conservation prioritization. We used land‐use change simulations to explore robustness of species’ habitat networks to alternative development scenarios. We applied our method to 14 vertebrate focal species of periurban Montreal, Canada. Accounting for connectivity in spatial prioritization strongly modified conservation priorities and the modified priorities were robust to uncertain climate change. Setting conservation priorities based on habitat quality and connectivity maintained a large proportion of the region's connectivity, despite anticipated habitat loss due to climate and land‐use change. The application of connectivity criteria alongside habitat‐quality criteria for protected‐area design was efficient with respect to the amount of area that needs protection and did not necessarily amplify trade‐offs among conservation criteria. Our approach and results are being applied in and around Montreal and are well suited to the design of ecological networks and green infrastructure for the conservation of biodiversity and ecosystem services in other regions, in particular regions around large cities, where connectivity is critically low.     

The Value of Using Feasibility Models in Systematic Conservation Planning to Predict Landholder Management Uptake

Understanding the social dimensions of conservation opportunity is crucial for conservation planning in multiple‐use landscapes. However, factors that influence the feasibility of implementing conservation actions, such as the history of landscape management, and landholders’ willingness to engage are often difficult or time consuming to quantify and rarely incorporated into planning. We examined how conservation agencies could reduce costs of acquiring such data by developing predictive models of management feasibility parameterized with social and biophysical factors likely to influence landholders’ decisions to engage in management. To test the utility of our best‐supported model, we developed 4 alternative investment scenarios based on different input data for conservation planning: social data only; biological data only; potential conservation opportunity derived from modeled feasibility that incurs no social data collection costs; and existing conservation opportunity derived from feasibility data that incurred collection costs. Using spatially explicit information on biodiversity values, feasibility, and management costs, we prioritized locations in southwest Australia to control an invasive predator that is detrimental to both agriculture and natural ecosystems: the red fox (Vulpes vulpes). When social data collection costs were moderate to high, the most cost‐effective investment scenario resulted from a predictive model of feasibility. Combining empirical feasibility data with biological data was more cost‐effective for prioritizing management when social data collection costs were low (<4% of the total budget). Calls for more data to inform conservation planning should take into account the costs and benefits of collecting and using social data to ensure that limited funding for conservation is spent in the most cost‐efficient and effective manner.     

Geography and Recovery under the U.S. Endangered Species Act

Abstract: The U.S. Endangered Species Act (ESA) defines an endangered species as one “at risk of extinction throughout all or a significant portion of its range.” The prevailing interpretation of this phrase, which focuses exclusively on the overall viability of listed species without regard to their geographic distribution, has led to development of listing and recovery criteria with fundamental conceptual, legal, and practical shortcomings. The ESA's concept of endangerment is broader than the biological concept of extinction risk in that the “esthetic, ecological, educational, historical, recreational, and scientific” values provided by species are not necessarily furthered by a species mere existence, but rather by a species presence across much of its former range. The concept of “significant portion of range” thus implies an additional geographic component to recovery that may enhance viability, but also offers independent benefits that Congress intended the act to achieve. Although the ESA differs from other major endangered‐species protection laws because it acknowledges the distinct contribution of geography to recovery, it resembles the “representation, resiliency, and redundancy” conservation‐planning framework commonly referenced in recovery plans. To address representation, listing and recovery standards should consider not only what proportion of its former range a species inhabits, but the types of habitats a species occupies and the ecological role it plays there. Recovery planning for formerly widely distributed species (e.g., the gray wolf [Canis lupus]) exemplifies how the geographic component implicit in the ESA's definition of endangerment should be considered in determining recovery goals through identification of ecologically significant types or niche variation within the extent of listed species, subspecies, or “distinct population segments.” By linking listing and recovery standards to niche and ecosystem concepts, the concept of ecologically significant type offers a scientific framework that promotes more coherent dialogue concerning the societal decisions surrounding recovery of endangered species.     

不同扰动下外源磷在形态磷间的分布规律

研究了3种扰动方式对外源磷在上覆水、间隙水、底泥中的数量分布的影响,并分析了内源磷形态间的转化过程.结果表明,与对照实验相比,物理扰动能促进上覆水中磷向底泥迁移,并高于生物扰动和组合扰动的促进作用,并且,三者均高于对照实验.这可归因于溶解氧的渗入.物理扰动能降低间隙水中DIP的平均含量,相比对照实验降低了12.13%(第6 d和第10 d平均值),但降低程度不如生物扰动(38.63%)和组合扰动(50.79%).3种扰动均能促进Fe/Al-P和Ca-P的形成,其中,物理扰动下Fe/Al-P和Ca-P平均形成量最大,物理扰动下AAP含量一直在减小,暗示物理扰动明显促进了AAP向闭蓄态的Fe/Al-P或者Ca-P转化.     

天津冬季一次重污染过程颗粒物中水溶性离子粒径分布特征

为研究天津冬季重污染天气过程中颗粒物水溶性离子的粒径谱分布及二次离子生成机制,于2014年1月利用Anderson撞击式分级采样器在中国气象局天津大气边界层观测站内采集颗粒物样品,并使用离子色谱仪分析Na~+、NH_4~+、K~+、Mg~(2+)、Ca~(2+)、Cl~-、NO_3~-、SO_4~(2-)等8种水溶性无机离子(TWSII).结果表明,采样期间PM_(2.5)和PM_(10)质量浓度均值分别为(138±100)μg·m~(-3)和(227±142)μg·m~(-3),粗、细粒子中TWSII的平均浓度分别为(34.07±6.16)μg·m~(-3)和(104.16±51.76)μg·m~(-3).细粒子中SO_4~(2-)、NO_3~-和NH_4~+这3种离子的浓度远高于其他离子,且相关性较好,粗粒子中NO_3~-、SO_4~(2-)、Cl~-浓度较高.随着污染程度加剧,细粒子中TWSII浓度增加明显,粗粒子中则变化不大.水溶性离子的粒径谱分布显示,SO_4~(2-)以单模态分布,优良天峰值出现在0.43~0.65μm,NO_3~-在优良日呈现三模态分布,峰值分别出现在0.43~0.65、2.1~3.3和5.8~9.0μm,NH_4~+呈双模态分布,优良日峰值出现在0.43~0.65μm和4.7~5.8μm,污染日3种二次离子峰值均以0.65~1.1μm的单模态分布为主,与三者之间的热动力平衡过程有关.细粒子中NH_4~+除与SO_4~(2-)和NO_3~-结合外,还与部分Cl~-结合,粗粒子中NH_4~+全部与NO_3~-和SO_4~(2-)结合后,剩余的NO_3~-和SO_4~(2-)与其他阳离子结合.     

供水管网铁释放的影响因素相对重要性分析

供水管网的铁释放问题近年来日益受到关注.影响供水管网铁释放的因素众多,本研究选取了pH、溶解氧(DO)、碱度(Alk)、硬度、氯离子(Cl~-)、硫酸根(SO_4~(2-))、温度(T)和水力停留时间(HRT)这8个影响因素,利用逐步回归和偏相关分析,建立了铁释放的非线性幂指数模型,并用标准化回归系数和偏相关系数比较了这些因素的相对重要性.结果表明,铁释放模型拟合效果良好;用标准化回归系数衡量影响因素相对重要性时,SO_4~(2-)、HRT、Cl~-和T是相对重要的因素,相对重要性排序为SO_4~(2-)≈HRTCl~-T;用偏相关系数衡量时,HRT、SO_4~(2-)、Alk、Cl~-和T是相对重要的因素,排序为HRTSO_4~(2-)AlkCl~-T.综合两种系数的分析结果,HRT、SO_4~(2-)和Cl~-是本研究条件下相对重要的因素,T和Alk的相对重要性视实际情况变动.     

北京市地下水中典型抗生素分布特征与潜在风险

应用气相色谱与质谱联用仪分析了北京市地下水内磺胺类(SAs)、氟喹诺酮类(FQs)、四环素类(TCs)、β-内酰胺类和大环内酯类等5大类抗生素.结果表明,北京地下水抗生素以SAs、FQs和TCs等3类为主,检出率分别为78.9%、100%和47.3%,其中甲氧苄氨嘧啶(TMP)、环丙沙星(CIP)和诺氟沙星(NOR)检出率均在70%以上.污灌区地下水样点抗生素浓度显著高于水源地和南水回灌区样点,磺胺二甲嘧啶(SDD)和磺胺嘧啶(SDZ)在污灌区检出峰值达到236 ng·L-1和96.8ng·L-1.生态风险评价结果表明,研究区地下水抗生素污染风险较小,但在污灌区显示中等或高等风险等级.CIP应作为研究区地下水抗生素监测中的重点监测对象.