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Development of the Karun macroinvertebrate tolerance index (KMTI) for semi-arid mountainous streams in Iran.
Fathi, Pejman; Dorche, Eisa Ebrahimi; Kashkooli, Omid Beyraghdar; Stribling, James; Bruder, Andreas.
Afiliação
  • Fathi P; Department of Natural Resources, Isfahan University of Technology, 84156-83111, Isfahan, Iran.
  • Dorche EE; Department of Natural Resources, Isfahan University of Technology, 84156-83111, Isfahan, Iran. e_ebrahimi@iut.ac.ir.
  • Kashkooli OB; Department of Natural Resources, Isfahan University of Technology, 84156-83111, Isfahan, Iran.
  • Stribling J; Center for Ecological Sciences, Tetra Tech, Inc, Owings Mills, MD, 21117, USA.
  • Bruder A; Laboratory of Applied Microbiology, University of Applied Sciences and Arts of Southern Switzerland, Manno, Switzerland.
Environ Monit Assess ; 194(6): 421, 2022 May 11.
Article em En | MEDLINE | ID: mdl-35543765
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%.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Rios / Invertebrados Tipo de estudo: Prognostic_studies Limite: Animals País como assunto: Asia Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Rios / Invertebrados Tipo de estudo: Prognostic_studies Limite: Animals País como assunto: Asia Idioma: En Ano de publicação: 2022 Tipo de documento: Article