Characterization of biological responses under different environmental conditions: A hierarchical modeling approach

A natural river system is organized as a nested hierarchy of interconnected habitats with specific environmental conditions to which the biological community has adapted. Due to this hierarchical structure, identifying the role of different stressors on the biological community is a formidable task. Efforts trying to link stressors to biological integrity have always been bound to the geographic scale of the selected study area, leading to scale-specific results. In this research, an attempt is made to lift this limitation and develop a hierarchical, scale-sensitive methodology that can identify the significant environmental stressors to the biological community at different scales. Sites with similar background environmental conditions are clustered using self-organizing maps (SOM). This is used to identify stressors which affect the biological community throughout the area of study - called environmental gradients or large-scale stressors. Subsequently, these clusters of similar observations (sampling sites) are progressively sub-divided using environmental variables with a significant but localized effect on the biological community - called small-scale stressors. A parent group of sites is split only when the resulting sub-groups have significantly different biological responses. At the end of this recursive sites decomposition procedure, the original set of observations is organized as a tree of environmentally homogeneous groups of observations characterized by unique biological responses to multiple stressors with different geographic extents. The developed hierarchical analysis methodology has been validated using a large-size dataset of environmental observations from the State of Ohio. Our results show that habitat degradation and increased nutrient loading are the large-scale stressors with a widespread impact in Ohio. Other stressors, such as heavy metals, pH or nitrate concentrations have significant albeit localized effects on biological integrity.     

Ecosystem states: Creating a data-derived, ecosystem-scale ecological response model that is explicit in space and time

Increasing difficulties associated with balancing consumptive demands for water and achieving ecological benefits in aquatic ecosystems provide opportunities for new ecosystem-scale ecological response models to assist managers. Using an Australian estuary as a case study, we developed a novel approach to create a data-derived state-and-transition model. The model identifies suites of co-occurring birds, fish, benthic invertebrates and aquatic macrophytes (as ‘states’) and the changing physico-chemical conditions that are associated with each (‘transitions’). The approach first used cluster analysis to identify sets of co-occurring biota. Differences in the physico-chemical data associated with each state were identified using classification trees, with the biotic distinctness of the resultant statistical model tested using analysis of similarities. The predictive capacity of the model was tested using new cases. Two models were created using different time-steps (annual and quarterly) and then combined to capture both longer-term trends and more-recent declines in ecological condition. We identified eight ecosystem states that were differentiated by a mix of water-quantity and water-quality variables. Each ecosystem state represented a distinct biotic assemblage under well-defined physico-chemical conditions. Two ‘basins of attraction’ were identified, with four tidally-influenced states, and another four independent of tidal influence. Within each basin, states described a continuum of relative health, manifest through declining taxonomic diversity and abundances. The main threshold determining relative health was whether freshwater flows had occurred in the region during the previous 339 days. Canonical analyses of principal coordinates tested the predictive capacity of the model and demonstrated that the variance in the environmental data set was well captured (87%) with 52% of the variance in the biological data set also captured. The latter increased to >80% when long- and short-term biological data were analysed separately, indicating that the model described the available data for the Coorong well. This approach thus created a data-derived, multivariate model, where neither states nor transitions were determined a priori. The approach did not over-fit the data, was robust to patchy or missing data, the choice of initial clustering technique and random errors in the biological data set, and was well-received by local natural resource managers. However, the model did not capture causal relationships and requires additional testing, particularly during future episodes of ecological recovery. The approach shows significant promise for simplifying management definitions of ecological condition and, via scenario analyses, can be used to assist in manager decision-making of large, complex aquatic ecosystems in the future.     

Incorporating low-resolution historic species location data decreases performance of distribution models

Developing robust species distribution models is important as model outputs are increasingly being incorporated into conservation policy and management decisions. A largely overlooked component of model assessment and refinement is whether to include historic species occurrence data in distribution models to increase the data sample size. Data of different temporal provenance often differ in spatial accuracy and precision. We test the effect of inclusion of historic coarse-resolution occurrence data on distribution model outputs for 187 species of birds in Australian tropical savannas. Models using only recent (after 1990), fine-resolution data had significantly higher model performance scores measured with area under the receiver operating characteristic curve (AUC) than models incorporating both fine- and coarse-resolution data. The drop in AUC score is positively correlated with the total area predicted to be suitable for the species (R² = 0.163-0.187, depending on the environmental predictors in the model), as coarser data generally leads to greater predicted areas. The remaining unexplained variation is likely to be due to the covariate errors resulting from resolution mismatch between species records and environmental predictors. We conclude that decisions regarding data use in species distribution models must be conscious of the variation in predictions that mixed-scale datasets might cause.     

"参考声级校准法"研究初探

如何解决环境噪声监测系统的自动校准问题,是困扰现阶段我国环境噪声自动监测工作走向实用化的难题.文章首次提出"参考声级校准法"的概念,并利用数字校准的方式实现了自动声级校准,对"参考声级校准法"实现方法作了简要的说明与阐述.     

三峡地区工业行业环境与经济行为特征分析

通过对三峡地区工业污染物产生强度和排放强度分析表明,三峡地区的产生强度和排放强度均高于东部先进水平和全国平均水平,指出了三峡地区的污染防治需从源头控制和末端治理双管齐下进行;引入"环境协调指数EHI(行业的经济贡献率与污染贡献率的比值)"分析行业的环境与经济行为特征,指明了三峡地区COD和氨氮相对污染重、经济效益差的行业.为区域产业结构调整提供了科学依据.     

创新我国环境监测质量管理体系初探

在调研国内外环境监测质量管理模式的基础上,提出了强化环境监测统一监督管理,建立国家环境监测质量管理与技术中心、区域(流域)环境监测质量管理与技术中心,完善各级环境监测机构质量管理工作体系的设想.     

“十一五”期间甘南县环境噪声变化及防治对策

以甘南县环境监测站"十一五"期间环境噪声监测结果为依据,对县域的环境噪声进行分析,并提出了防治对策。     

关于水污染物排放总量的核定

系统分析了水污染物排放总量核定不同层次的信息关联和相关的特征信息间的相互关系，指出城市是最基本的总量核定对象；阐述了现行环境监测与环境管理制度对水污染物排放总量核定存在着不完全信息问题；指出环境管理部门不掌握的部分排污总量信息对总量控制整体的影响相当大，应对排污总量进行全面、科学的核定。     

产品生命周期环境成本核算实例研究

针对项目层次环境成本核算技术存在的2大缺陷，构建了产品生命周期环境成本核算模型。以红矾钠产品生命周期为例，通过企业的实地调查，以直接计算法、费用当量法进行拟合，得到了红矾钠产品（产量2.28万t/a)生命周期环境成本为，直接计算法：5084.48万元；费用当量法：4941.43万元。分析了环境成本核算对可持续发展决策和管理的作用和意义。