基于底栖动物预测模型构建生物完整性指数评价河流健康

提高生物完整性指数(integrity of biotic index,IBI; 又称多参数指数multi-metric index, MMI)在时间和空间尺度的稳定性是水生态完整性评价和水环境管理实践的重要内容.本研究利用2004—2011年在浙江省多个河流采集的227个点位的底栖动物和水质理化数据,利用地理信息系统(GIS)提取样点及其对应流域的自然预测因子(如地理形态学、气候学)和土地利用数据,通过随机森林模型方法定量时间和空间尺度自然变量对生物群落的影响,构建基于预测模型控制自然因子影响方法和常规方法的MMI,并比较它们的表现力.结果表明: 基于预测模型法和基于常规方法构建的MMI的核心组成参数存在差异,随机森林模型中自然预测因子对预测模型MMI构成生物参数的解释量介于11.4%～61.2%.预测模型方法提高了MMI的精确度和准确度,但其敏感性和响应性低于常规方法的MMI.最近距离方法表明,9个评价点位和1个严重受损点位的自然属性与参照点位的自然属性存在差异性.在计算参照点位自然属性代表性范围的基础上,采用预测模型方法控制自然变量可以提高MMI的精确度和准确度,同时降低评价结果出现Ⅰ型(将健康水体误判为受损水体)或者Ⅱ型(将受损水体误判为健康水体)错误的可能性.研究结果可以为提高完整性指数评价稳定性和表现力提供方法支持.

Predictive model based multimetric index of macroinvertebrates for river health assessment

Improving the stability of integrity of biotic index (IBI; i.e., multi-metric indices, MMI) across temporal and spatial scales is one of the most important issues in water ecosystem integrity bioassessment and water environment management. Using datasets of field-based macroinvertebrate and physicochemical variables and GIS-based natural predictors (e.g., geomorphology and climate) and land use variables collected at 227 river sites from 2004 to 2011 across the Zhejiang Province, China, we used random forests (RF) to adjust the effects of natural variations at temporal and spatial scales on macroinvertebrate metrics. We then developed natural variations adjusted (predictive) and unadjusted (null) MMIs and compared performance between them. The core me-trics selected for predictive and null MMIs were different from each other, and natural variations within core metrics in predictive MMI explained by RF models ranged between 11.4% and 61.2%. The predictive MMI was more precise and accurate, but less responsive and sensitive than null MMI. The multivariate nearest-neighbor test determined that 9 test sites and 1 most degraded site were flagged outside of the environmental space of the reference site network. We found that combination of predictive MMI developed by using predictive model and the nearest-neighbor test performed best and decreased risks of inferring type I (designating a water body as being in poor biological condition, when it was actually in good condition) and type II (designating a water body as being in good biological condition, when it was actually in poor condition) errors. Our results provided an effective method to improve the stability and performance of integrity of biotic index.