朱砂叶螨对三种杀螨剂的抗性选育与抗性风险评估

为评价朱砂叶螨Tetranychus cinnabarinus对3种杀螨剂的抗性风险,在实验室抗性品系选育基础上,应用数量遗传学中的域性状分析法,研究了朱砂叶螨北碚种群对甲氰菊酯、阿维菌素和哒螨灵3种杀螨剂的抗性现实遗传力,并对3种药剂在不同杀死率下抗性发展的速率进行了预测。结果表明：分别单一连续汰选16代后,朱砂叶螨对甲氰菊酯、阿维菌素的抗性倍数分别达26.54和4.51倍,对哒螨灵表现为敏感性降低（抗性倍数为1.16倍）;朱砂叶螨对甲氰菊酯、阿维菌素和哒螨灵的抗性现实遗传力分别为0.2472,0.1519和0.0160。在室内选择条件下,杀死率为50%～90%时,要获得10倍抗性,甲氰菊酯仅需要13～6代,阿维菌素需要约21～10代;哒螨灵需要约197～89代;在田间选择,三种药剂都将需要更长的时间。抗性筛选16代结果表明,抗性风险较高的是菊酯类的甲氰菊酯,其次是生物源农药阿维菌素,杂环类的哒螨灵抗性风险较小。试验结果可为朱砂叶螨抗性治理提供参考。     

转基因植物的生态风险

转基因植物已在很多国家大规模商业化种植,并且取得了显著的经济效益。同时有关转基因植物潜在的生态风险已引起广泛的关注。本文从转基因植物人侵危害、对非靶标有益生物直接和间接的影响、害虫对抗虫转基因植物产生抗性、抗病毒转基因植物带来的潜在风险等方面论述了转基因植物可能潜在的生态安全性问题。     

布氏田鼠摄食量、累积摄食量与日龄的关系

布氏田鼠 (Ｍｉｃｒｏｔｕｓｂｒａｎｄｔｉ)是内蒙古典型草原区的主要害鼠 ,其危害方式主要表现之一为与牲畜争夺牧草资源[1,4 ,11,12 ] 。准确地测定其日食量(ＤＦＣ)与累积摄食量 (ＣＦＣ) ,对于定量地衡量该鼠的危害程度 ,进一步推算鼠害防治的经济阈值有着重要的意义。一些学者着手有关该鼠食性与食量 ,以及非取食性牧草消耗量的研究工作[3 ,7～ 9] 。但由于受实验手段的限制 ,一直未能解决布氏田鼠日食量、累积摄食量与田鼠年龄的关系等难题。本文以测定典型草原区布氏田鼠日食量与体重的关系 ,结合布氏田鼠体重与年龄的数学关系方程 …     

长江口中华鲟保护区鱼卵和仔鱼的分布特征

根据2004年5月和8月共2个航次对中华鲟（Acipenser sinensis）保护区及其邻近水域16个定点观测站进行的鱼卵和仔鱼调查资料,对该水域鱼卵仔鱼的种类组成和数量分布的季节变化特征以及有关的环境影响因子作了分析和探讨。结果表明：鱼卵仔鱼的种类组成和数量分布呈现明显的季节变化特征。春夏季种类组成变化大,种类数春季高于夏季;其中,以凤鲚(Coilia mystus)卵和仔鱼分布最多,其变动直接影响总量的变化;平面分布范围夏季较春季小,数量主要集中于崇明岛北部的北支水域和崇明东滩的东南部水域,崇明岛南部沿岸的北港水域相对较少;由于调查水域受长江径流的作用十分明显,因而数量分布和种类组成上随径流的变化表现出较明显的季节变动;南、北支水系的水文条件不同,海水由东向西楔入的程度不同,产生的环境状况影响了鱼卵仔鱼的平面分布格局。     

黄河三角洲石油化工区农田土壤-玉米体系PAHs的分布特征及风险评价

为明确黄河三角洲石油开采区表层土壤和玉米中多环芳烃（PAHs）的含量及其污染水平,采集农田土壤和玉米各71个样品,检测农田土壤和玉米各部位中16种PAHs含量,并采用内梅罗指数法和健康风险评价模型评估了农田土壤中多环芳烃的生态健康风险。结果表明,农田土壤、玉米根、茎和叶中多环芳烃的含量分别为256.6-1936、291.4-680.9、324.9-527.9、289.5-2400 μg/kg。农田土壤中多环芳烃以4-6环为主。多环芳烃在玉米根茎叶富集系数大小排序为：叶 > 茎 > 根。玉米不同组织中PAHs浓度与相应农田土壤中PAHs浓度的进行相关分析结果表明,农田土壤中PAHs含量与玉米根、茎中PAHs含量均存在极显著正相关关系,相关系数分别为0.98（P<0.01）、0.98（P<0.01）,表明玉米根和茎的多环芳烃主要来源于农田土壤中,农田土壤中PAHs的含量影响着PAHs在玉米根茎中的积累和分布。玉米叶中PAHs含量与农田土壤中PAHs含量与玉米根、茎中PAHs含量不存在相关关系,表明玉米叶中多环芳烃并非来自土壤中PAHs的迁移,可能来源于大气。内梅罗指数结果表明,农田土壤PAHs达到了中度污染,其中BaA、Pyr和BbF达到了偏重污染;健康风险评价结果表明,农田土壤PAHs对儿童和成人的平均非致癌风险分别为0.44和0.12（均小于1）,表明农田土壤多环芳烃对成人和儿童的非致癌风险是可接受;农田土壤PAHs对儿童和成人的平均致癌风险分别为3.6×10^-5、9.0×10^-6,没有超过致癌风险水平上限（10^-4）,致癌风险尚在可接受范围内。3种暴露途径中,皮肤接触是土壤PAHs的最主要暴露方式,其次是经口摄食,吸入暴露途径甚微,可忽略不计。PAHs对儿童健康的威胁风险要大于成人,所以应尽可能避免儿童直接接触或误食土壤等其他介质的污染物。     

A structured analysis of risk to important wildlife elements in three Australian Wildlife Conservancy sanctuaries

Where data and information are lacking, structured expert risk assessments can provide a powerful tool to progress natural resource planning. In many situations, practitioners make informal assessments of risk within small groups that typically constitute employees. In this study, we report on three small (in terms of experts) structured expert‐based risk assessment case studies conducted by expert employees of a not‐for‐profit organisation (Australian Wildlife Conservancy) to demonstrate the utility of the approach. The case studies were carried out for three wildlife sanctuaries managed by AWC: Faure Island, Karakamia and Paruna. The likelihood that a set of direct risk factors would cause management failure for sets of important wildlife elements in the three sanctuaries was elicited from the small group of ecological experts. The analysis was couched in terms of a management aim to not lose species from each wildlife element over the management period of 25 years with current management. The experts believed, in particular, that increasing temperature and decreasing water availability associated with climate change were likely to impact significantly upon the vegetation elements and water‐reliant fauna associated with the sanctuaries. Some vegetation elements were also thought likely to be at risk of over‐grazing, unsuitable fire regimes and, in some cases, disease. In addition to predation by exotic predators at one sanctuary, the experts identified additional direct risk factors for various fauna elements associated with expected changes to the vegetation elements, including reduction in food availability, nesting habitat, and generally important life media. From the risk analyses, a preliminary conceptual model was developed to underpin monitoring and to indicate areas for possible management intervention and research. The case studies demonstrate that even in a small workplace team, structured risk assessments can be efficiently accomplished and can provide expedient and transparent information that effectively captures and aggregates the views of the experts.     

Metals and arsenic in marine fish commercialized in the NE Brazil: Risk to human health

Abstract

Arsenic, cadmium, lead, and mercury in fish is the result of long-term biomagnification in the food chain and is of public concern, due to the toxicity they engender. The objective of this research was to determine the concentrations of arsenic, cadmium, lead, and mercury in 13 species of marine fish broadly commercialized in Aracaju, SE, Brazil and to evaluate the risks of fish consumption associated with these trace elements, using the Target Hazard Quotient (THQ). As, Cd, and Pb levels were measured with inductively coupled plasma mass spectrometry (ICP-MS), and mercury was analyzed via cold vapor atomic absorption spectrometry. The results indicate a large variability in concentrations for arsenic (0.07–2.03?mg kg^–1) and mercury (0.01–1.44?mg kg^–1), associated with the animal dietary category. Cadmium (0.04–0.19?mg kg^–1) and lead (<0.01–0.45?mg kg^–1), on the other hand showed a mild variability. None of the evaluated specimens had As, Cd, and Pb THQ values higher than 1. The THQ values for mercury were higher but indicated no consumption risk, except for amberjack, and snook fish. Overall THQ indicates lower risk of consumption in fish that are at the base of the food chain, than in those that are top predators.     

沙地樟子松人工林土壤理化性质与微生物生物量的动态变化

为揭示不同林龄沙地樟子松人工林土壤理化性质和微生物生物量的动态和相互关系,以毛乌素沙地、科尔沁沙地和呼伦贝尔沙地不同林龄樟子松人工林为对象,分析土壤理化性质、土壤微生物生物量碳和微生物生物量氮变化规律。结果表明:樟子松人工林土壤理化性质随林龄增加在不同沙地中表现不同,毛乌素沙地土壤容重和养分含量明显降低,科尔沁沙地土壤孔隙度和养分含量明显升高,呼伦贝尔沙地土壤养分则呈现先增加后降低趋势。与土壤理化性质变化趋势类似,毛乌素沙地樟子松人工林土壤微生物生物量氮随着林龄的增加而降低,科尔沁沙地土壤微生物生物量氮随着林龄的增加而升高,呼伦贝尔沙地土壤微生物生物量氮随着林龄的增加呈先增加而后降低趋势。影响毛乌素沙地、科尔沁沙地和呼伦贝尔沙地土壤微生物生物量碳、氮的主要因子分别是硝态氮、铵态氮和有机质含量。毛乌素与科尔沁沙地樟子松人工林主要限制因子为土壤氮,而呼伦贝尔沙地樟子松受土壤有机碳限制较强。     

Using Bayesian networks to incorporate uncertainty in habitat suitability index models

Habitat suitability index (HSI) models rarely characterize the uncertainty associated with their estimates of habitat quality despite the fact that uncertainty can have important management implications. The purpose of this paper was to explore the use of Bayesian belief networks (BBNs) for representing and propagating 3 types of uncertainty in HSI models—uncertainty in the suitability index relationships, the parameters of the HSI equation, and measurement of habitat variables (i.e., model inputs). I constructed a BBN–HSI model, based on an existing HSI model, using Netica™ software. I parameterized the BBN's conditional probability tables via Monte Carlo methods, and developed a discretization scheme that met specifications for numerical error. I applied the model to both real and dummy sites in order to demonstrate the utility of the BBN–HSI model for 1) determining whether sites with different habitat types had statistically significant differences in HSI, and 2) making decisions based on rules that reflect different attitudes toward risk—maximum expected value, maximin, and maximax. I also examined effects of uncertainty in the habitat variables on the model's output. Some sites with different habitat types had different values for E[HSI], the expected value of HSI, but habitat suitability was not significantly different based on the overlap of 90% confidence intervals for E[HSI]. The different decision rules resulted in different rankings of sites, and hence, different decisions based on risk. As measurement uncertainty in habitat variables increased, sites with significantly different (α = 0.1) E[HSI] became statistically more similar. Incorporating uncertainty in HSI models enables explicit consideration of risk and more robust habitat management decisions. © 2012 The Wildlife Society.     

A Regional Multiple-Stressor Rank-Based Ecological Risk Assessment for the Fjord of Port Valdez,Alaska

We conducted an ecological risk assessment of the marine environment of Port Valdez, a fjord in south-central Alaska. Because the assessment was regional rather than site-specific and contained a large number of different stressors in a variety of environments, we required a nontraditional method to estimate risks. We created a Relative Risk Model to rank and sum individual risks numerically within each subarea, from each source, and to each habitat. Application of this model involved division of Port Valdez into 11 subareas containing specific ecological and anthropogenic structures and activities. Within each subarea, the stressor sources were analyzed to estimate exposure of receptors within habitats leading to effects relevant to the chosen assessment endpoints. The subareas were analyzed and compared to form a Port-wide perspective of ecological risk. Available chemical concentrations from sediment and mussels collected from the Port were compared to various toxicological benchmarks as a partial confirmation of the risk analysis. An estimation of the risk of polycyclic aromatic hydrocarbons (PAHs) to marine invertebrates indicated low risk. The municipal boat harbor had the highest estimate, which reflected our relative risk rankings. The Relative Risk Model approach appears robust and has potential for use in situations where multiple stressors are of concern and for assessments covering broad geographic areas. In the Port Valdez assessment the approach provided relative risk rankings for chemical and physical stressors from various sources. But data were available for confirmation of risk estimates only for the chemical stressors. The rankings are relative, and extrapolation beyond the scenario in which they were developed is not warranted. Uncertainty is large, and the numerical scores collapse a multidimensional space into a single value. Use of just the numerical score out of context is more valid than with other indexes. The value of the approach lies in the relative rankings and the accounting of the components of the relative risk score.