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伊朗半干旱山区卡伦大型无脊椎动物耐受指数(KMTI)的发展。

Development of the Karun macroinvertebrate tolerance index (KMTI) for semi-arid mountainous streams in Iran.

机构信息

Department of Natural Resources, Isfahan University of Technology, 84156-83111, Isfahan, Iran.

Center for Ecological Sciences, Tetra Tech, Inc, Owings Mills, MD, 21117, USA.

出版信息

Environ Monit Assess. 2022 May 11;194(6):421. doi: 10.1007/s10661-022-09834-8.

Abstract

The most robust approach to ecological monitoring and assessment is the use of regionally calibrated indicators. These should be calculated based on collocated biological (response) and physicochemical (stressor) variables and an objective rating and scoring system. In developing countries, a frequent lack of financial and technical resources for monitoring has led to many environmental problems being overlooked, such as the degradation of streams, rivers, and watersheds. In this paper, we propose the Karun Macroinvertebrate Tolerance Index (KMTI) for application to rivers in the Karun River basin, which is the largest watershed in Iran, draining semi-arid mountainous regions. The KMTI is the first biological index specifically developed and calibrated for Iranian water resources. Benthic macroinvertebrates, physical habitat, hydromorphic, and water quality data were collected and measured at 54 sites across four seasons in 2018 and 2019. A total of 101 families of benthic macroinvertebrates belonging to eight classes and 21 orders were identified, and tolerance values were determined for 95 families. The KMTI was found to be most efficient in identifying ecological degradation when data were used from winter samples with a discrimination efficiency (DE) 90% and a four-season mean of 84.3%. Also, the best DE of the water quality classification table based on the KMTI index was equal to 86.9%.

摘要

最稳健的生态监测和评估方法是使用区域性校准指标。这些指标应基于生物(响应)和物理化学(胁迫)变量的共置数据,并采用客观的评级和评分系统进行计算。在发展中国家,由于缺乏监测的财力和技术资源,导致许多环境问题被忽视,例如溪流、河流和流域的退化。在本文中,我们提出了卡拉恩大型无脊椎动物耐受指数(KMTI),用于伊朗最大的流域——卡拉恩河流域的河流,该流域位于半干旱山区。KMTI 是专为伊朗水资源开发和校准的第一个生物指标。2018 年和 2019 年的四个季节,在 54 个地点收集和测量了底栖大型无脊椎动物、物理生境、水形态和水质数据。共鉴定出 101 个底栖大型无脊椎动物科,属于 8 个纲和 21 个目,并确定了 95 个科的耐受值。结果表明,当使用冬季样本数据时,KMTI 最有效地识别生态退化,其判别效率(DE)为 90%,四季均值为 84.3%。此外,基于 KMTI 指数的水质分类表的最佳 DE 等于 86.9%。

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