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.     

Development of a coupled reservoir operation and water diversion model: Balancing human and environmental flow requirements

Maintaining a natural flow regime helps preserve the health of riverine ecosystems. Conventional studies on reservoir operations have focused mainly on identifying optimal operational schemes for satisfying human water demands. To systematically reflect the ecological effects of both natural and human-induced hydrologic alterations, water diversions downstream of the reservoirs should be considered as well. This research focused on a coupled reservoir operation and water diversion (CROWD) model, created through the integration of a reservoir operation model and a water diversion model. The proposed model considers both human and environmental flow requirements, and represents a compromise that balances ecological protection (preservation of the natural flow regime of a river) and human needs (reduced water shortages). In the reservoir operation model, the reservoir space is divided into three zones and different operating rules are developed for directing reservoir operation when water levels are in different zones; in the water diversion model, different water users are assigned different supply priorities with the instream flows no more than the minimum environmental flows having the highest priority; and the two models are coupled by the water mass balance between the two hydraulic facilities. The non-dominated-sorting genetic algorithm II (NSGA-II) was used to determine the parameters of the developed CROWD model and the model was applied to support the joint operational management of the Tanghe Reservoir and the Liaoyang Diversion in the Tang river basin, China. The resulting reservoir operation and water diversion schemes indicate that the CROWD model is useful for optimizing the operation of reservoirs and water diversion schemes. Moreover, it helps to analyze tradeoffs between human and environmental water needs, resulting in solutions that reduce the risk of water shortages and minimize ecological integrity disturbances.     

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.     

Regulatory attitudes and environmental innovation in a model combining internal and external R&D

The extent to which environmental regulatory institutions are either ‘green’ or ‘brown’ impacts not just the intensity of regulation at any moment, but also the incentives for the development of new pollution-control technologies. We set up a strategic model of R&D in which a polluter can deploy technologies developed in-house, or license technologies developed by specialist outsiders (an ‘eco-industry’). Polluters exert R&D effort and may even develop redundant technologies to improve the terms on which they procure technology from outside. We find that, while regulatory bias has an ambiguous impact on the best-available technology, strategic delegation to systematically biased regulators can improve social welfare.     

厦门水库水体颗粒物分布特征及其与环境因子的关系研究

研究了厦门湖边水库、石兜-坂头水库水体颗粒物的分布特征及其与环境因子之间的关系。结果表明:(1)水体颗粒物平均含量以坂头库区最高,石兜库区次之,湖边水库最低,平均含量分别为31.9、27.7和23.1mg/L;在空间分布上,不同水库或库区、不同采样站位间,由于水体颗粒物的来源成因不同,其分布规律呈现出明显的差异。(2)从水体颗粒物与环境因子的关联度分析,湖边水库及石兜-坂头水库两个库区的水体颗粒物与总氮和总磷都有较大关联性。(3)利用Pearson积矩相关系数(两尾)进行检验,湖边水库及石兜-坂头水库两个库区的水体颗粒物均与总氮呈显著或极显著相关,与透明度呈显著负相关,与叶绿素a均呈负相关,与其它因子的相关规律性不明显。(4)水体颗粒物与环境因子的逐步回归分析表明,在不同的水库或库区,对水体颗粒物有显著影响的环境因子各不相同,湖边水库是高锰酸盐指数和总氮,石兜库区是高锰酸盐指数、总氮和总磷,坂头库区是pH、溶解氧和总磷。     

关于环境监测学术研究的思考

阐述了环境监测学术研究的重要意义,指出了当前存在的主要问题,探讨了开展学术研究的主要途径与方法,提出了环境监测学术研究的方向性课题。     

论开展主要环境质量指标浓度考核

针对现行环境质量考核体系中存在问题,选取主要的环境质量考核指标,提出逐步推行浓度值考核设想,并对比分析该考核办法与现行比例考核办法的优劣。     

铅锌冶炼废水处理后沉渣重金属元素特征与环境活性研究

通过扫描电子显微镜与X射线衍射仪分析、BCR法三步连续提取及元素化学分析,对铅锌冶炼废水处理后沉渣中重金属的赋存状态、环境活性进行研究。结果表明,沉渣中主要毒害元素为Zn、Pb、Mn、Cd和Cu,且以非晶态存在,其含量分别为62350、29530、7650、4530和830 mg/kg。渣中Zn和Cd酸可提取态所占比例较大,分别为37.72%和34.44%; Mn的可还原态相对含量很高,达到49.67%;Zn和Pb的可氧化态含量较高,分别占全量的34.16%、34.68%;Pb和Cu的残渣态含量较高,分别占全量的39.72%和61.69%。     

地市级环境监测管理信息系统自主开发的探索——桂林市环境监测中心站基于EXCEL服务器平台方案实践

桂林环境监测站利用Excel服务器+SQL-Server数据库的组合方案,成功开发了"环境监测管理信息系统"。在环境监测信息化的实用软件设计、开发模式创新、核心技术把握、人才队伍建设、维护运行机制等多方面进行积极探索。     

突发环境事件应急监测管理体系的研究

从突发环境事件应急监测主要任务、预案管理、管理体制、运行机制和法制建设等方面阐述环境应急监测管理“一案三制”建设中存在的问题和改进建议.