中美淡水生物区系中汞物种敏感度分布比较

通过收集无机汞对中国与美国淡水水生生物的毒性数据,构建了脊椎动物(包括鱼类)、无脊椎动物(包括节肢动物和非节肢无脊椎动物)及所有物种对汞的物种敏感度分布(SSD:species sensitivity distributions)曲线,并在此基础上对中国和美国不同类别生物对汞的敏感性分布进行了分析.结果表明:中国与美国各类生物及所有物种对汞的SSD敏感性分布曲线没有显著差异.然而,中国淡水水生物种对汞短期暴露的HC5(hazardous concentration for 5% of the species)较美国淡水物种的阈值小,尤其是非节肢无脊椎动物,汞对美国非节肢动物的HC5值是我国对应物种的7.4倍.在保护95%的物种水平下,中国不同类别试验生物对汞的敏感性排序为无脊椎动物>脊椎动物,其中节肢动物>非节肢无脊椎动物>鱼类;而对应的美国生物对汞的敏感性排序无脊椎动物>脊椎动物,其中节肢动物>鱼类>非节肢无脊椎动物.另外,中美所有节肢动物对汞的敏感性要强于所有鱼类和所有非节肢无脊椎动物.所以在使用所有物种推导水质基准时应考虑其中各类别物种敏感度分布的影响,且需要注意采用美国淡水水生物种推导的水质基准可能会对我国淡水水生物种造成"保护不足".     

植物内重金属的赋存形态及分布特征分析方法研究进展

植物内重金属分析不仅有助于阐述重金属迁移转化规律,揭示植物修复机理,也是监测评价大气、水及土壤环境重金属污染状况的重要依据。研究针对植物样品中重金属赋存形态及分布特征的分析方法进行综述,分别介绍了其原理、优缺点及应用。提出制定统一高效的分析检测方法,深化植物内重金属的赋存形态及分布特征与环境相关性的研究以及建立数据共享平台是植物样品重金属信息分析方法的未来发展方向。     

甘南高寒草甸植物群落物种多度分布特征

为分析甘南高寒草甸植物群落物种多度分布格局对海拔梯度的响应,探讨物种多样性的形成规律及维持机制,采用典型样地调查法采集数据信息,并通过RAD软件程序包对实验数据进行拟合分析.结果表明:植物群落物种多样性随海拔的升高呈现先增加后降低的"中间膨胀"模式,海拔3500m处群落物种多样性达到最大.利用5个模型对海拔梯度上全部物...     

中国遗传资源优先保护等级评价标准的构建

确定科学合理的生物遗传资源优先保护等级,是当前保护生物学研究的一个核心问题。文章通过分析国内外遗传资源优先保护等级评价研究进展,针对遗传资源的特点,对遗传资源与物种关系、遗传资源多样性、境外种群影响、遗传资源价值、潜在利用价值、人工繁育技术、特有情况等方面进行了探讨并提出相应建议,初步确定了遗传资源优先保护等级的评价标准,以期能为生物遗传资源的有效保护和科学管理提供参考依据。     

矿化垃圾混配种植介质的盆栽实验研究

矿化垃圾营养成分和有机质含量较高,但含有重金属和盐分.为了解矿化垃圾的施加比例对植物的生长影响以及植物对矿化垃圾混配种植介质中营养成分的吸收和重金属的富集,将矿化垃圾和本地绿化土按照不同比例混配,研究混配种植介质的理化性质的改善条件、矿化垃圾对植物体内的生物量和叶绿素含量的影响、植物对混配种植介质中营养成分的吸收量以及植物体内各种重金属累积浓度,探索矿化垃圾的最佳施用比例.实验结果表明,矿化垃圾可改善上海本地土壤贫瘠的普遍状况,其添加质量分数在50%～75%合适,对植物生长有利.     

GIS支持下的秦岭植被景观梯度分析

秦岭植被景观类型丰富,具有过渡性和复杂性特点,植被垂直分带明显. 在分析了大尺度秦岭植被景观空间水平分布格局的基础上,利用基于GIS梯度分析方法,分析秦岭的植被与海拔梯度的关系,得到秦岭不同植被景观类型的斑块数、分布范围、植被分布的海拨高度平均值和标准差,并以太白山为例,对太白山植物种进行了梯度分析. 结果表明,随着海拔高度的增加,太白山依次出现7种植被景观类型:温带草丛→温带落叶灌丛→温带落叶阔叶林→亚热带针叶林→亚热带和热带山地针叶林→草甸→高寒草甸等植被类型,植物种亦发生相应的变化.      

添加不同钝化剂降低污泥堆肥的植物毒性研究

高温堆肥后污泥的土地利用可充分发挥资源化、无害化的优势,污泥土地利用防止重金属污染一直是污泥施用中最重要的问题。通过测验发芽率指数这一代表植物毒性的指标证明,通过在污泥堆肥中加入粉煤灰、磷矿粉和草炭等不同的改性剂或重金属钝化剂可大大降低堆肥的植物毒性,从而降低污泥中重金属的可利用性。     

胜利电厂一期工程环境影响回顾评价——污染源和污染治理措施回顾评价

本文论述了胜利电厂污染源和污染治理措施回顾评价的方法和内容。回顾评价采用现场调查,与一期环评报告进行对比分析的方法进行,重点放在污染源、污染治理措施的调查分析上。     

Scaling range sizes to threats for robust predictions of risks to biodiversity

Assessments of risk to biodiversity often rely on spatial distributions of species and ecosystems. Range‐size metrics used extensively in these assessments, such as area of occupancy (AOO), are sensitive to measurement scale, prompting proposals to measure them at finer scales or at different scales based on the shape of the distribution or ecological characteristics of the biota. Despite its dominant role in red‐list assessments for decades, appropriate spatial scales of AOO for predicting risks of species’ extinction or ecosystem collapse remain untested and contentious. There are no quantitative evaluations of the scale‐sensitivity of AOO as a predictor of risks, the relationship between optimal AOO scale and threat scale, or the effect of grid uncertainty. We used stochastic simulation models to explore risks to ecosystems and species with clustered, dispersed, and linear distribution patterns subject to regimes of threat events with different frequency and spatial extent. Area of occupancy was an accurate predictor of risk (0.81<|r|<0.98) and performed optimally when measured with grid cells 0.1–1.0 times the largest plausible area threatened by an event. Contrary to previous assertions, estimates of AOO at these relatively coarse scales were better predictors of risk than finer‐scale estimates of AOO (e.g., when measurement cells are <1% of the area of the largest threat). The optimal scale depended on the spatial scales of threats more than the shape or size of biotic distributions. Although we found appreciable potential for grid‐measurement errors, current IUCN guidelines for estimating AOO neutralize geometric uncertainty and incorporate effective scaling procedures for assessing risks posed by landscape‐scale threats to species and ecosystems.     

Integrating intraseasonal grassland dynamics in cross-scale distribution modeling to support waterbird recovery plans

Despite much discussion about the utility of remote sensing for effective conservation, the inclusion of these technologies in species recovery plans remains largely anecdotal. We developed a modeling approach for the integration of local, spatially measured ecosystem functional dynamics into a species distribution modeling (SDM) framework in which other ecologically relevant factors are modeled separately at broad scales. To illustrate the approach, we incorporated intraseasonal water-vegetation dynamics into a cross-scale SDM for the Common Snipe (Gallinago gallinago), which is highly dependent on water and vegetation dynamics. The Common Snipe is an Iberian grassland waterbird characteristic of European agricultural meadows and a member of one of the most threatened bird guilds. The intraseasonal dynamics of water content of vegetation were measured using the standard deviation of the normalized difference water index time series computed from bimonthly images of the Sentinel-2 satellite. The recovery plan for the Common Snipe in Galicia (northwestern Iberian Peninsula) provided an opportunity to apply our modeling framework. Model accuracy in predicting the species’ distribution at a regional scale (resulting from integration of downscaled climate projections with regional habitat–topographic suitability models) was very high (area under the curve [AUC] of 0.981 and Boyce's index of 0.971). Local water-vegetation dynamic models, based exclusively on Sentinel-2 imagery, were good predictors (AUC of 0.849 and Boyce's index of 0.976). The predictive power improved (AUC of 0.92 and Boyce's index of 0.98) when local model predictions were restricted to areas identified by the continental and regional models as priorities for conservation. Our models also performed well (AUC of 0.90 and Boyce's index of 0.93) when projected to updated water-vegetation conditions. Our modeling framework enabled incorporation of key ecosystem processes closely related to water and carbon cycles while accounting for other factors ecologically relevant to endangered grassland waterbirds across different scales, allowed identification of priority areas for conservation, and provided an opportunity for cost-effective recovery planning by monitoring management effectiveness from space.