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首页> 外文期刊>Environmental toxicology and chemistry >A geographic information systems-based, weights-of-evidence approach for diagnosing aquatic ecosystem impairment.
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A geographic information systems-based, weights-of-evidence approach for diagnosing aquatic ecosystem impairment.

机译：基于地理信息系统的证据权重方法，用于诊断水生生态系统的损害。

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摘要

A Geographic Information Systems-based, watershed-level assessment using Bayesian weights of evidence (WOE) and weighted logistic regression (WLR) provides a method to determine and compare potential environmental stressors in lotic ecosystems and to create predictive models of general or species-specific biological impairment across numerous spatial scales based on limited existing sample data. The WOE/WLR technique used in the present study is a data-driven, probabilistic approach conceptualized in epidemiological research and both developed for and currently used in minerals exploration. Extrapolation of this methodology to a case-study watershed assessment of the Great and Little Miami watersheds (OH, USA) using archival data yielded baseline results consistent with previous assessments. The method additionally produced a quantitative determination of physical and chemical watershed stressor associations with biological impairment and a predicted comparative probability (i.e., favorability) of biological impairment at a spatial resolution of 0.5 km2 over the watershed study region. Habitat stressors showed the greatest spatial association with biological impairment in low-order streams (on average, 56% of total spatial association), whereas water chemistry, particularly that of wastewater effluent, was associated most strongly with biological impairment in high-order reaches (on average, 79% of total spatial association, 28% of which was attributed to effluent). Significant potential stressors varied by land-use and stream order as well as by species. This WOE/WLR method provides a highly useful "tier 1" watershed risk assessment product through the integration of various existing data sources, and it produces a clear visual communication of areas favorable for biological impairment and a quantitative ranking of candidate stressors and associated uncertainty.
机译：使用贝叶斯证据权重（WOE）和加权逻辑回归（WLR）的基于地理信息系统的分水岭级评估，提供了一种方法来确定和比较抽水生态系统中的潜在环境压力源，并创建通用或特定物种的预测模型基于有限的现有样本数据，在多个空间尺度上进行生物损伤。本研究中使用的WOE / WLR技术是一种数据驱动的概率方法，在流行病学研究中得到了概念化，并且是为矿产勘查开发的，目前正在使用。使用档案数据将该方法外推至大和小迈阿密流域（美国俄亥俄州）的案例研究分水岭评估，得出的基线结果与以前的评估相符。该方法还可以在流域研究区域以0.5 km2的空间分辨率定量确定具有生物损伤的物理和化学分水岭胁迫源的关联性以及预测的生物损伤的比较概率（即，可喜度）。生境应激源在低序河流中显示出与生物损害最大的空间关联（平均占总空间关联的56％），而水化学，尤其是废水的化学反应与高阶河流中的生物损害最相关（平均而言，占总空间关联的79％，其中28％来自废水。潜在的潜在压力因土地利用和溪流顺序以及物种而异。通过集成各种现有数据源，此WOE / WLR方法提供了非常有用的“第1级”分水岭风险评估产品，并且它对有利于生物损伤的区域进行了清晰的视觉传达，并对候选压力源和相关不确定性进行了定量排名。

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