土壤与酸的反应时间对土壤酸度及土壤溶液中铝离子形态分布的影响

用两种土壤研究土壤与酸的反应时间对土壤酸度及土壤溶液中铝离子形态分布的影响。结果表明,土壤溶液pH随反应时间增加而增加,安徽红壤pH的增幅大于江西红壤。在pH3.0的HNO3溶液中,土壤溶液中的总铝及Al3 均随反应时间增加而减少,而在pH3．5的HNO3溶液中,土壤溶液中总铝及Al－F络合物先随反应时间的增加而减小．然后又有所增加。     

羊角月芽藻摄取磷形态的动力学研究

将湖水中的磷分成总溶解磷,溶解反应磷,总反应磷、溶解的水解性磷,溶解的光解性磷。研究其在羊角月芽过程中的变化。     

加强预警监测体系建设 高效应对突发生态环境问题

简述了区域突发生态环境问题的现状与特点以及环境监测应对突发生态环境问题的现状,指出了环境监测应对突发生态环境污染存在的问题。提出,应加快预警监测技术体系建设,丰富预警监测技术手段,重视生物生态监测在预警监测中的作用,提高环境质量综合评价水平,发挥环境信息系统在预警监测中的作用,加强预警监测技术人才培养。     

生态补水对玄武湖水质的影响

分析南京玄武湖1997年-2010年的水质变化,以及生态补水与玄武湖水质变化的关系。结果表明,玄武湖于1998年实施生态补水,随着生态补水的持续运行和生态补水量的不断增加,玄武湖水质得以显著改善,其水质类别由生态补水前的劣V类水体转为V类水体,并接近于Ⅳ类水体,富营养化程度由重度富营养化水平转为轻度富营养化水平。目前玄武湖主要营养物质来源于生态补水,相关性分析显示各湖区TN浓度与生态补水中TN浓度呈显著相关关系。     

农药企业搬迁土地挥发性有机物风险评价

对常州某农药生产场地土壤中挥发性有机物(VOCs)污染状况以及健康风险和生态风险进行了调查评价。结果表明,该场地土壤中挥发性有机物污染以苯系物和卤代烃为主。苯系物质量浓度为0~56.6 mg/kg,卤代烃质量浓度为0~1.14 mg/kg。健康风险评价结果均在可接受范围内,而生态风险评价显示生产车间内的土壤VOCs生态风险较大,存在着对生物的危害。     

Using Multi-Scale Sampling and Spatial Cross-Correlation to Investigate Patterns of Plant Species Richness

Land managers need better techniques to assess exoticplant invasions. We used the cross-correlationstatistic, I _YZ, to test for the presence ofspatial cross-correlation between pair-wisecombinations of soil characteristics, topographicvariables, plant species richness, and cover ofvascular plants in a 754 ha study site in RockyMountain National Park, Colorado, U.S.A. Using 25 largeplots (1000 m²) in five vegetation types, 8 of 12variables showed significant spatial cross-correlationwith at least one other variable, while 6 of 12variables showed significant spatial auto-correlation. Elevation and slope showed significant spatialcross-correlation with all variables except percentcover of native and exotic species. Percent cover ofnative species had significant spatialcross-correlations with soil variables, but not withexotic species. This was probably because of thepatchy distributions of vegetation types in the studyarea. At a finer resolution, using data from ten1 m² subplots within each of the 1000 m² plots, allvariables showed significant spatial auto- andcross-correlation. Large-plot sampling was moreaffected by topographic factors than speciesdistribution patterns, while with finer resolutionsampling, the opposite was true. However, thestatistically and biologically significant spatialcorrelation of native and exotic species could only bedetected with finer resolution sampling. We foundexotic plant species invading areas with high nativeplant richness and cover, and in fertile soils high innitrogen, silt, and clay. Spatial auto- andcross-correlation statistics, along with theintegration of remotely sensed data and geographicinformation systems, are powerful new tools forevaluating the patterns and distribution of native andexotic plant species in relation to landscape structure.     

太湖沉积物重金属污染及生态风险性评价

对1993年～1999年所采集的太湖表层沉积物中重金属含量进行了统计分析和生态风险性评价.结果表明,太湖沉积物的pH值呈中性至弱碱性,较适合于沉积物中粘土矿物及腐殖质对重金属的吸附;太湖大部分地区沉积物未受到重金属污染,且沉积物中重金属处于安全状态.沉积物生态风险性指数评价结果也表明,太湖大部分地区目前无重金属生态危害.     

Predicting occurrence of juvenile shark habitat to improve conservation planning

Fishing and habitat degradation have increased the extinction risk of sharks, and conservation strategies recognize that survival of juveniles is critical for the effective management of shark populations. Despite the rapid expansion of marine protected areas (MPAs) globally, the paucity of shark‐monitoring data on large scales (100s–1000s km) means that the effectiveness of MPAs in halting shark declines remains unclear. Using data collected by baited remote underwater video systems (BRUVS) in northwestern Australia, we developed generalized linear models to elucidate the ecological drivers of habitat suitability for juvenile sharks. We assessed occurrence patterns at the order and species levels. We included all juvenile sharks sampled and the 3 most abundant species sampled separately (grey reef [Carcharhinus amblyrhynchos], sandbar [Carcharhinus plumbeus], and whitetip reef sharks [Triaenodon obesus]). We predicted the occurrence of juvenile sharks across 490,515 km² of coastal waters and quantified the representation of highly suitable habitats within MPAs. Our species‐level models had higher accuracy (? ≥ 0.69) and deviance explained (≥48%) than our order‐level model (? = 0.36 and deviance explained of 10%). Maps of predicted occurrence revealed different species‐specific patterns of highly suitable habitat. These differences likely reflect different physiological or resource requirements between individual species and validate concerns over the utility of conservation targets based on aggregate species groups as opposed to a species‐focused approach. Highly suitable habitats were poorly represented in MPAs with the most restrictions on extractive activities. This spatial mismatch possibly indicates a lack of explicit conservation targets and information on species distribution during the planning process. Non‐extractive BRUVS provided a useful platform for building the suitability models across large scales to assist conservation planning across multiple maritime jurisdictions, and our approach provides a simple for method for testing the effectiveness of MPAs.     

Prevalence of the Crayfish Plague Pathogen Aphanomyces astaci in Invasive American Crayfishes in the Czech Republic

Abstract:  In Central Europe invasive North American crayfishes are carriers of the oomycete Aphanomyces astaci, which causes crayfish plague. This lethal disease currently represents one of the major threats to native European crayfishes. We used molecular methods—species-specific amplification and sequencing of the pathogen DNA—to investigate the prevalence of individuals latently infected with A. astaci in 28 populations of two invasive American crayfish species (6 of the signal crayfish [ Pacifastacus leniusculus ] and 22 of the spiny-cheek crayfish [ Orconectes limosus ]) in the Czech Republic. The pathogen occurred in 17 investigated populations. We recorded a high variation in positive reactions, ranging from 0% to 100%, in populations of O. limosus . In P. leniusculus, however, only one individual out of 124 tested positive for the pathogen. There was a clear relationship between the water body type and pathogen prevalence in O. limosus . Infection ratios in isolated standing waters were usually low, whereas in running waters, pathogen prevalence often exceeded 50%. Other evaluated characteristics of potential plague pathogen carriers (size, sex, and the presence of melanized spots in the cuticle) seemed to be unrelated to infection. Our data suggest that in contrast to other European countries, O. limosus seems to be the primary reservoir of crayfish plague in the Czech Republic. Although all populations of alien American crayfishes may be potential sources of infections and should be managed as such, knowledge on the prevalence of the plague pathogen at various localities may allow managers to focus conservation efforts on the most directly endangered populations of native crayfishes.     

Modelling plant species richness using functional groups

Conservation biologists increasingly rely on spatial predictive models of biodiversity to support decision-making. Therefore, highly accurate and ecologically meaningful models are required at relatively broad spatial scales. While statistical techniques have been optimized to improve model accuracy, less focus has been given to the question: How does the autecology of a single species affect model quality? We compare a direct modelling approach versus a cumulative modelling approach for predicting plant species richness, where the latter gives more weight to the ecology of functional species groups. In the direct modelling approach, species richness is predicted by a single model calibrated for all species. In the cumulative modelling approach, the species were partitioned into functional groups, with each group calibrated separately and species richness of each group was cumulated to predict total species richness. We hypothesized that model accuracy depends on the ecology of individual species and that the cumulative modelling approach would predict species richness more accurately. The predictors explained plant species richness by ca. 25%. However, depending on the functional group the deviance explained varied from 3 to 67%. While both modelling approaches performed equally well, the models of the different functional groups highly varied in their quality and their spatial richness pattern. This variability helps to improve our understanding on how plant functional groups respond to ecological gradients.